Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 150, Issue , 2019, Pages 115-134

  Hossain, Mohammad D ^a   Chen, Dongmei ^a  

^a QUEEN S UNIVERSITY   (Canada)

Author keywords

Geographic object; High spatial resolution; Image segmentation; OBIA; Remote sensing

Indexed keywords

IMAGE ANALYSIS; PIXELS; REMOTE SENSING;

COMPUTATIONALLY EFFICIENT; FEATURE EXTRACTION AND CLASSIFICATION; GEOGRAPHIC OBJECTS; HIGH SPATIAL RESOLUTION; OBIA; OBJECT BASED IMAGE ANALYSIS; OBJECT BASED IMAGE ANALYSIS (OBIA); REMOTE SENSING IMAGE PROCESSING;

IMAGE SEGMENTATION;

ALGORITHM; IKONOS; IMAGE ANALYSIS; IMAGE PROCESSING; LANDSAT; PIXEL; REMOTE SENSING; SATELLITE IMAGERY; SEGMENTATION; SPATIAL RESOLUTION;

EID: 85061903227     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2019.02.009     Document Type: Short Survey

References (383)

1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S., SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34 (2012), 2274–2281, 10.1109/TPAMI.2012.120.
2. Adams, R., Bischof, L., Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16 (1994), 641–647, 10.1109/34.295913.
3. Akçay, H.G., Aksoy, S., Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Trans. Geosci. Remote Sens. 46 (2008), 2097–2111, 10.1109/TGRS.2008.916644.
4. Al-Hujazi, E.H., Sood, A.K., 1991. Integration of edge- and region-based techniques for range image segmentation. In: Casasent, D.P. (Ed.), SPIE 1381, Intelligent Robots and Computer Vision IX: Algorithms and Techniques. International Society for Optics and Photonics, pp. 589–599. https://doi.org/10.1117/12.25190.
5. Albrecht, F., 2010. Uncertainty in image interpretation as reference for accuracy assessment in object-based image analysis. In: Ninth International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, pp. 13–16.
6. Alshehhi, R., Marpu, R.P., Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images. ISPRS J. Photogramm. Remote Sens. 126 (2017), 245–260, 10.1016/j.isprsjprs.2017.02.008.
7. Anders, N.S., Seijmonsbergen, A.C., Bouten, W., Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping. Remote Sens. Environ. 115 (2011), 2976–2985, 10.1016/J.RSE.2011.05.007.
8. Andrey, P., Tarroux, P., Unsupervised segmentation of Markov random field modeled textured images using selectionist relaxation. IEEE Trans. Pattern Anal. Mach. Intell. 20 (1998), 252–262, 10.1109/34.667883.
9. Audebert, N., Le Saux, B., Lefèvre, S., 2016. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. In: Lai, S., Lepetit, V., Nishino, K., Sato, Y. (Eds.), Asian Conference on Computer Vision. Springer, Cham, pp. 180–196. https://doi.org/10.1007/978-3-319-54181-5_12.
10. Auquilla, A., Heremans, S., Vanegas, P., Van Orshoven, J., A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index. International Conference on Computational Science and Its Applications, 2014, Springer, Cham, 360–375, 10.1007/978-3-319-09144-0_25.
11. Baatz, M., Schäpe, A., Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Angew. Geogr. informationsverarbeitung XII 58 (2000), 12–23.
12. Ballard, D.H., Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit. 13 (1981), 111–122, 10.1016/0031-3203(81)90009-1.
13. Banerjee, B., Varma, S., Buddhiraju, K.M., Satellite image segmentation: a novel adaptive mean-shift clustering based approach. Geoscience and Remote Sensing Symposium (IGARSS), 2012, IEEE International, 4319–4322.
14. Barrile, V., Bilotta, G., Fast extraction of roads for emergencies with segmentation of satellite imagery. Procedia -Social Behav. Sci. 223 (2016), 903–908, 10.1016/j.sbspro.2016.05.313.
15. Beaulieu, J.M., Goldberg, M., Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 150–163, 10.1109/34.16711.
16. Bellens, R., Gautama, S., Martinez-Fonte, L., Philips, W., Chan, J.C.W., Canters, F., Improved classification of VHR images of urban areas using directional morphological profiles. IEEE Trans. Geosci. Remote Sens. 46 (2008), 2803–2813, 10.1109/TGRS.2008.2000628.
17. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M., Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58 (2004), 239–258, 10.1016/j.isprsjprs.2003.10.002.
18. Beveridge, J.R., Griffith, J., Kohler, R.R., Hanson, A.R., Riseman, E.M., Segmenting images using localized histograms and region merging. Int. J. Comput. Vis. 2 (1989), 311–347, 10.1007/BF00158168.
19. Bins, L.S.A., Fonseca, L.M.G., Erthal, G.J., Ii, F.M., Satellite Imagery Segmentation : a region growing approach. Anais VIII Simposia Brasilerio de Sensoriamenta Remoto. Slavador, Brazil, 1996, 677–680.
20. Blaschke, T., Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2010), 2–16, 10.1016/j.isprsjprs.2009.06.004.
21. Blaschke, T., Charles, B., Pekkarinen, A., 2004. Remote sensing image analysis: including the spatial domain. In: de Jong, S.M., van der Meer, F.D. (Ed.), Remote Sensing Image Analysis: Including the Spatial Domain. Springer Netherlands, pp. 211–236. https://doi.org/10.1017/S0032247400010123.
22. Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., Van Der Meer, F., Van Der Werff, H., Van Coillie, F., Tiede, D., Geographic Object-Based Image Analysis - Towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 87 (2014), 180–191, 10.1016/j.isprsjprs.2013.09.014.
23. Blaschke, T., Lang, S., Hay, G.J. (Eds.), 2008. Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer Science & Business Media.
24. Böhner, J., Selige, T., Ringeler, A., Image segmentation using representativeness analysis and region growing. Boehner, J., McCloy, K.R., Strobl, J., (eds.) SAGA - Analyses and Modelling Applications, 2006, Göttinger Geographische Abhandlungen, Göttingen, Germany, 29–38.
25. Bouman, C.A., Shapiro, M., A multiscale random field model for Bayesian image segmentation. IEEE Trans. Image Process. 3 (1994), 162–177, 10.1109/83.277898.
26. Bunting, P., Clewley, D., Lucas, R.M., Gillingham, S., The Remote Sensing and GIS Software Library (RSGISLib). Comput. Geosci. 62 (2014), 216–226, 10.1016/J.CAGEO.2013.08.007.
27. Burnett, C., Blaschke, T., A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecol. Modell. 168 (2003), 233–249, 10.1016/S0304-3800(03)00139-X.
28. Byun, Y., Kim, D., Lee, J., Kim, Y., A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging. Int. J. Remote Sens. 32 (2011), 4589–4609, 10.1080/01431161.2010.489066.
29. Câmara, G., Souza, R.C.M., Freitas, U.M., Garrido, J., Spring: Integrating remote sensing and gis by object-oriented data modelling. Comput. Graph. 20 (1996), 395–403, 10.1016/0097-8493(96)00008-8.
30. Campbell, J.B., Wynne, R.H., Introduction to Remote Sensing. 5th ed., 2011, The Guilford press, New York.
31. Canny, J., A computational approach to edge detection. Readings in Computer Vision, 1987, Elsevier, 184–203, 10.1016/B978-0-08-051581-6.50024-6.
32. Cánovas-García, F., Alonso-Sarría, F., A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery. Geocarto Int. 30 (2015), 937–961, 10.1080/10106049.2015.1004131.
33. Cao, W., Li, J., Liu, J., Zhang, P., 2016. Two improved segmentation algorithms for whole cardiac CT sequence images. In: International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, pp. 346–351.
34. Carleer, A.P., Debeir, O., Wolff, E., Assessment of very high spatial resolution satellite image segmentations. Photogramm. Eng. Remote Sens. 71 (2005), 1285–1294 https://doi.org/10.14358/PERS.71.11.1285.
35. Castilla, G., Hay, G.J., Image objects and geographic objects. Blaschke, T., Lang, S., Hay, G.J., (eds.) Object-Based Image Analysis, 2008, Springer, Berlin Heidelberg, Berlin, Heidelberg, 91–110, 10.1007/978-3-540-77058-9_5.
36. Castilla, G., Hay, G.J., Ruiz-Gallardo, J.R., Size-constrained Region Merging (SCRM): an automated delineation tool for assisted photointerpretation. Photogramm. Eng. Remote Sens. 74 (2008), 409–419 https://doi.org/10.14358/PERS.74.4.409.
37. Chaudhuri, D., Kushwaha, N.K., Samal, A., Semi-automated road detection from high resolution satellite images by directional morphological enhancement and segmentation techniques. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5 (2012), 1538–1544, 10.1109/JSTARS.2012.2199085.
38. Cheevasuvit, F., Maitre, H., Vidal-Madjar, D., A robust method for picture segmentation based on a split-and-merge procedure. Comput. Vision Graph. Image Process. 34 (1986), 268–281, 10.1016/S0734-189X(86)80042-1.
39. Chen, B., Qiu, F., Wu, B., Du, H., Image segmentation based on constrained spectral variance difference and edge penalty. Remote Sens. 7 (2015), 5980–6004, 10.3390/rs70505980.
40. Chen, D., 2018. LAGISA Online GeoSegment Tool [WWW Document]. URL http://130.15.95.215/lagisa/ (accessed 9.25.18).
41. Chen, G., Weng, Q., Hay, G.J., He, Y., Geographic Object-based Image Analysis (GEOBIA): Emerging trends and future opportunities. GIScience Remote Sens. 55 (2018), 159–182, 10.1080/15481603.2018.1426092.
42. Chen, J., Deng, M., Mei, X., Chen, T., Shao, Q., Hong, L., Optimal segmentation of a high-resolution remote-sensing image guided by area and boundary. Int. J. Remote Sens. 35 (2014), 6914–6939, 10.1080/01431161.2014.960617.
43. Chen, J., Li, J., Pan, D., Zhu, Q., Mao, Z., Edge-guided multiscale segmentation of satellite multispectral imagery. IEEE Trans. Geosci. Remote Sens. 50 (2012), 4513–4520, 10.1109/TGRS.2012.2194502.
44. Chen, J., Pan, D., Mao, Z., Image-object detectable in multiscale analysis on high-resolution remotely sensed imagery. Int. J. Remote Sens. 30 (2009), 3585–3602, 10.1080/01431160802585348.
45. Chen, K.-M., Chen, S.-Y., Color texture segmentation using feature distributions. Pattern Recognit. Lett. 23 (2002), 755–771, 10.1016/S0167-8655(01)00150-7.
46. Chen, P.C., Pavlidis, T., Segmentation by texture using a co-occurrence matrix and a split-and-merge algorithm. Comput. Graph. Image Process. 10 (1979), 172–182, 10.1016/0146-664X(79)90049-2.
47. Chen, R., Li, X., Li, J., Object-based features for house detection from RGB high-resolution images. Remote Sens. 10 (2018), 1–24, 10.3390/rs10030451.
48. Chen, S.-Y., Lin, W.-C., Chen, C.-T., Split-and-merge image segmentation based on localized feature analysis and statistical tests. CVGIP Graph. Model. Image Process. 53 (1991), 457–475, 10.1016/1049-9652(91)90030-N.
49. Chen, S., Luo, J., Shen, Z., Hu, X., Gao, L., 2008. Segmentation of multi-spectral satellite images based on watershed algorithm. In: 2008 International Symposium on Knowledge Acquisition and Modeling, KAM 2008. IEEE, pp. 684–688. https://doi.org/10.1109/KAM.2008.84.
50. Chen, X., Chen, J., Yamaguchi, Y., 2012. Soft image segmentation model. In: Proc. Int. Conf. Comput. Vis. Remote Sensing, CVRS 2012, pp. 90–93. https://doi.org/10.1109/CVRS.2012.6421239.
51. Chen, Z., Zhao, Z., Gong, P., Zeng, B., A new process for the segmentation of high resolution remote sensing imagery. Int. J. Remote Sens. 27 (2006), 4991–5001, 10.1080/01431160600658131.
52. Cheng, H.D., Jiang, X.H., Sun, Y., Wang, J., Color image segmentation: Advances and prospects. Pattern Recognit. 34 (2001), 2259–2281, 10.1016/S0031-3203(00)00149-7.
53. Chen, Chu-Song, Wu, Ja-Ling, Hung, Yi-Ping, Theoretical aspects of vertically invariant gray-level morphological operators and their application on adaptive signal and image filtering. IEEE Trans. Signal Process. 47 (1999), 1049–1060, 10.1109/78.752602.
54. Chubey, M.S., Franklin, S.E., Wulder, M.A., Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogramm. Eng. Remote Sens. 72 (2006), 383–394 https://doi.org/10.14358/PERS.72.4.383.
55. Clinton, N., Holt, A., Scarborough, J., Yan, L., Gong, P., Accuracy assessment measures for object-based image segmentation goodness. Photogramm. Eng. Remote Sens. 76 (2010), 289–299 https://doi.org/10.14358/PERS.76.3.289.
56. Comaniciu, D., Meer, P., Mean shift: A robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), 603–619, 10.1109/34.1000236.
57. Costa, H., Foody, G.M., Boyd, D.S., Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sens. Environ. 205 (2018), 338–351, 10.1016/j.rse.2017.11.024.
58. Csillik, O., Fast segmentation and classification of very high resolution remote sensing data using. Remote Sens., 9, 2017, 19, 10.3390/rs9030243.
59. D'Elia, C., Poggi, G., Scarpa, G., A tree-structured Markov random field model for Bayesian image segmentation. IEEE Trans. image Process. 12 (2003), 1259–1273, 10.1109/TIP.2003.817257.
60. Davis, C.H., Wang, X., Planimetric accuracy of Ikonos 1 m panchromatic orthoimage products and their utility for local government GIS basemap applications. Int. J. Remote Sens. 24 (2003), 4267–4288, 10.1080/0143116031000070328.
61. Davis, L.S., Rosenfeld, A., Weszka, J.S., Region extraction by averaging and thresholding. IEEE Trans. Syst. Man. Cybern. SMC-5 (1975), 383–388, 10.1109/TSMC.1975.5408419.
62. De Smet, P., Pries, R.L.V.P.M., Implementation and analysis of an optimized rainfalling watershed algorithm. Electron. Imaging 8 (2000), 759–1166.
63. Deng, F.L., Tang, P., Liu, Y., Yang, C.J., Automated hierarchical segmentation of high-resolution remote sensing imagery with introduced relaxation factors. J. Remote Sens. 17 (2013), 1492–1507.
64. Deng, Y., Manjunath, B.S., Unsupervised segmentation of color-texture regions in images and video. IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001), 800–810, 10.1109/34.946985.
65. Deriche, M., Amin, A., Qureshi, M., Color image segmentation by combining the convex active contour and the Chan Vese model. Pattern Anal. Appl., 2017, 1–15, 10.1007/s10044-017-0632-9.
66. Deriche, R., Fast algorithms for low-level vision. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990), 78–87, 10.1109/34.41386.
67. Derivaux, S., Forestier, G., Wemmert, C., Lefvre, S., Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation. Pattern Recognit. Lett. 31 (2010), 2364–2374, 10.1016/j.patrec.2010.07.007.
68. Dey, V., Zhang, Y., Zhong, M., 2010. A review on image segmentation techniques with remote sensing perspective. In: Wagner, W., Székely, B. (Ed.), ISPRS TC VII Symposium – 100 Years ISPRS. Vienna, pp. 31–42.
69. Dey, V., Zhang, Y., Zhong, M., Salehi, B., Image segmentation techniques for urban land cover segmentation of VHR imagery: Recent developments and future prospects. Int. J. Geoinform. 9 (2013), 15–35.
70. Drǎguţ, L., Csillik, O., Eisank, C., Tiede, D., Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 88 (2014), 119–127, 10.1016/j.isprsjprs.2013.11.018.
71. Drǎguţ, L., Eisank, C., Strasser, T., Local variance for multi-scale analysis in geomorphometry. Geomorphology 130 (2011), 162–172, 10.1016/j.geomorph.2011.03.011.
72. Drǎguţ, L., Tiede, D., Levick, S.R., ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. Int. J. Geogr. Inf. Sci. 24 (2010), 859–871, 10.1080/13658810903174803.
73. Dronova, I., Gong, P., Clinton, N.E., Wang, L., Fu, W., Qi, S., Liu, Y., Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods. Remote Sens. Environ. 127 (2012), 357–369, 10.1016/j.rse.2012.09.018.
74. Du, S., Guo, Z., Wang, W., Guo, L., Nie, J., A comparative study of the segmentation of weighted aggregation and multiresolution segmentation. GIScience Remote Sens. 53 (2016), 1–20, 10.1080/15481603.2016.1215769.
75. Ďurikovič, R., Kaneda, K., Yamashita, H., Dynamic contour: A texture approach and contour operations. Vis. Comput. 11 (1995), 277–289, 10.1007/BF01898405.
76. Eisank, C., Smith, M., Hillier, J., Assessment of multiresolution segmentation for delimiting drumlins in digital elevation models. Geomorphology 214 (2014), 452–464, 10.1016/j.geomorph.2014.02.028.
77. Epshtein, B., Ofek, E., Wexler, Y., 2010. Detecting text in natural scenes with stroke width transform. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, pp. 2963–2970. https://doi.org/10.1109/CVPR.2010.5540041.
78. Esch, T., Thiel, M., Bock, M., Roth, A., Dech, S., Improvement of image segmentation accuracy based on multiscale optimization procedure. IEEE Geosci. Remote Sens. Lett. 5 (2008), 463–467, 10.1109/LGRS.2008.919622.
79. Espindola, G.M., Camara, G., Reis, I.a., Bins, L.S., Monteiro, a.M., Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation. Int. J. Remote Sens. 27 (2006), 3035–3040, 10.1080/01431160600617194.
80. Falah, R.K., Ph.Bolon, J.P.Cocquerez, 1994. A region-region and region-edge cooperative approach of image segmentation. In: International Conference on Image Processing. IEEE, Austin, Texas, Texas, pp. 470–474.
81. Fan, J., Yau, D.K., Elmagarmid, A.K., Aref, W.G., Automatic image segmentation by integrating color-edge extractionand seeded region growing. IEEE Trans. Image Process. 10 (2001), 1454–1466, 10.1109/83.951532.
82. Fan, J., Zeng, G., Body, M., Hacid, M.S., Seeded region growing: An extensive and comparative study. Pattern Recognit. Lett. 26 (2005), 1139–1156, 10.1016/j.patrec.2004.10.010.
83. Farag, A.A., Mohamed, R.M., El-Baz, A., A unified framework for MAP estimation in remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 43 (2005), 1617–1634, 10.1109/TGRS.2005.849059.
84. Farid, H., Simoncelli, E.P., Optimally rotation-equivariant directional derivative kernels. International Conference on Computer Analysis of Images and Patterns, 1997, Springer, Berlin, Heidelberg, 207–214, 10.1007/3-540-63460-6_119.
85. Felzenszwalb, P.F., Huttenlocher, D.P., Efficient graph-based image segmentation. Int. J. Comput. Vis. 59 (2004), 167–181, 10.1023/B:VISI.0000022288.19776.77.
86. Feng, W., Jia, J., Liu, Z.Q., Self-validated labeling of Markov random fields for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 32 (2010), 1871–1887, 10.1109/TPAMI.2010.24.
87. Fisher, P., The pixel: a snare and a delusion. Int. J. Remote Sens. 18:3 (1997), 679–685.
88. Fonseca-Luengo, D., García-Pedrero, A., Lillo-Saavedra, M., Costumero, R., Menasalvas, E., Gonzalo-Martín, C., 2014. Optimal scale in a hierarchical segmentation method for satellite images. In: International Conference on Rough Sets and Intelligent Systems Paradigms. Springer, pp. 351–358. https://doi.org/10.1007/978-3-319-08729-0_36.
89. Fosgate, C.H., Krim, H., Irving, W.W., Karl, W.C., Willsky, A.S., Multiscale segmentation and anomaly enhancement of SAR imagery. IEEE Trans. Image Process. 6 (1997), 7–20, 10.1109/83.552077.
90. Freixenet, J., Muñoz, X., Raba, D., Martí, J., Cufí, X., 2002. Yet another survey on image segmentation: Region and boundary information integration. Comput. Vision—ECCV 2002, pp. 21–25.
91. Gaetano, R., Masi, G., Poggi, G., Verdoliva, L., Scarpa, G., Marker-controlled watershed-based segmentation of multiresolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 53 (2015), 2987–3004, 10.1109/TGRS.2014.2367129.
92. Gaetano, R., Masi, G., Scarpa, G., Poggi, G., A marker-controlled watershed segmentation: Edge, mark and fill. International Geoscience and Remote Sensing Symposium (IGARSS), 2012, IEEE, Munich, Germany, 4315–4318, 10.1109/IGARSS.2012.6351713.
93. Gaetano, R., Scarpa, G., Poggi, G., Hierarchical texture-based segmentation of multiresolution remote-sensing images. IEEE Trans. Geosci. Remote Sens. 47 (2009), 2129–2141, 10.1109/TGRS.2008.2010708.
94. Gambotto, J.-P., A new approach to combining region growing and edge detection. Pattern Recognit. Lett. 14 (1993), 869–875.
95. Gao, H., Tang, Y., Jing, L., Li, H., Ding, H., A novel unsupervised segmentation quality evaluation method for remote sensing images. Sensors, 17, 2017, 2427, 10.3390/s17102427.
96. Gao, Y.A.N., Mas, J.F., Kerle, N., Navarrete Pacheco, J.A., Optimal region growing segmentation and its effect on classification accuracy. Int. J. Remote Sens. 32 (2011), 3747–3763, 10.1080/01431161003777189.
97. Geman, S., Graffigne, C., 1986. Markov random field image models and their applications to computer vision. In: International Congress of Mathematicians, pp. 1496–1517.
98. Ghita, O., Whelan, P.F., Computational approach for edge linking. J. Electron. Imaging, 11, 2002, 479, 10.1117/1.1501574.
99. Ghosh, A., Joshi, P.K., A comparison of selected classification algorithms for mappingbamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery. Int. J. Appl. Earth Obs. Geoinf. 26 (2014), 298–311, 10.1016/j.jag.2013.08.011.
100. Gofman, E., Developing an efficient region growing engine for image segmentation. Proceedings - International Conference on Image Processing, 2006, ICIP, 2413–2416, 10.1109/ICIP.2006.312949.
101. Golinkoff, J.S., Area dependent region merging: A novel, user-customizable method to create forest stands and strata. Eur. J. Remote Sens. 46 (2013), 511–533, 10.5721/EuJRS20134630.
102. Gonzalo-Martín, C., Lillo-Saavedra, M., Menasalvas, E., Fonseca-Luengo, D., García-Pedrero, A., Costumero, R., Local optimal scale in a hierarchical segmentation method for satellite images: An OBIA approach for the agricultural landscape. J. Intell. Inf. Syst. 46 (2016), 517–529, 10.1007/s10844-015-0365-4.
103. Grizonnet, M., Michel, J., Poughon, V., Inglada, J., Savinaud, M., Cresson, R., Orfeo ToolBox: open source processing of remote sensing images. Open Geospatial Data, Softw. Stand., 2, 2017, 15, 10.1186/s40965-017-0031-6.
104. Grybas, H., Melendy, L., Congalton, R.G., A comparison of unsupervised segmentation parameter optimization approaches using moderate- and high-resolution imagery. GIScience Remote Sens. 54 (2017), 515–533, 10.1080/15481603.2017.1287238.
105. Gu, H., Han, Y., Yang, Y., Li, H., Liu, Z., Soergel, U., Blaschke, T., Cui, S., An efficient parallel multi-scale segmentation method for remote sensing imagery. Remote Sens., 10, 2018, 590, 10.3390/rs10040590.
106. Guindon, B., Computer-based aerial image understanding: A review and assessment of its application to planimetric information extraction from very high resolution satellite images. Can. J. Remote Sens. 23 (1997), 38–47, 10.1080/07038992.1997.10874676.
107. Guo, Z., Du, S., Mining parameter information for building extraction and change detection with very high-resolution imagery and GIS data. GISci. Remote Sens. 54 (2017), 38–63, 10.1080/15481603.2016.1250328.
108. Hadavand, A., Saadatseresht, M., Homayouni, S., Segmentation parameter selection for object-based land-cover mapping from ultra high resolution spectral and elevation data. Int. J. Remote Sens. 38 (2017), 3586–3607, 10.1080/01431161.2017.1302107.
109. Happ, P.N., Feitosa, R.Q., Street, A., Assessment of optimization methods for automatic tuning of segmentation parameters. 4th International Conference on Geographic Object-Based Image Analysis. Rio de Janeiro, 2012, 490–495.
110. Happ, P.N., Ferreira, R.S., Costa, G.A.O.P., Feitosa, R.Q., Bentes, C., Farias, R., Achanccaray, P.M., 2016. Interseg: a distributed image segmentation tool. In: GEOBIA 2016: Solutions and Synergies. University of Twente Faculty of Geo-Information and Earth Observation (ITC). https://doi.org/10.3990/2.450.
111. Haralick, R.M., Edge and region analysis for digital image data. Image Modeling, 1981, Elsevier, 171–184, 10.1016/B978-0-12-597320-5.50014-0.
112. Haralick, R.M., Shapiro, L.G., Image Segmentation Techniques. Comput. Vision Graph. Image Process. 29 (1985), 100–132.
113. Haris, K., Efstratiadis, S.N., Maglaveras, N., Katsaggelos, A.K., Hybrid image segmentation using watersheds and fast region merging. IEEE Trans. Image Process. 7 (1998), 1684–1699, 10.1109/83.730380.
114. Hay, G.J., Blaschke, T., Marceau, D.J., Bouchard, A., A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS J. Photogramm. Remote Sens. 57 (2003), 327–345, 10.1016/S0924-2716(02)00162-4.
115. Hay, G.J., Castilla, G., Chapter 1.4 Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. Blaschke, T., Lang, S., Hay, G.J., (eds.) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, 2008, Springer Science & Business Media, 75–90.
116. Hay, G.J., Castilla, G., Object-Based Image Analysis: Strengths, Weaknesses, Opportunities and Threats (SWOT). 1st International Conference on Object-Based Image Analysis (OBIA 2006), 2006, 4–5, 10.1007/978-3-540-77058-9_4.
117. Hay, G.J., Castilla, G., Wulder, M.A., Ruiz, J.R., An automated object-based approach for the multiscale image segmentation of forest scenes. Int. J. Appl. Earth Obs. Geoinf. 7 (2005), 339–359, 10.1016/j.jag.2005.06.005.
118. He, J., Kim, C., Kuo, C.-C.J., Interactive Segmentation Techniques Algorithms and Performance Evaluation. 2014, Springer.
119. Heumann, B.W., An object-based classification of mangroves using a hybrid decision tree-support vector machine approach. Remote Sens. 3 (2011), 2440–2460, 10.3390/rs3112440.
120. Hofmann, P., Tiede, D., Image segmentation based on hexagonal sampling grids. South-Eastern Eur. J. Earth Obs. Geomat. 3 (2014), 173–177.
121. Horowitz, S.L., Pavlidis, T., Picture segmentation by a tree traversal algorithm. J. ACM 23 (1976), 368–388, 10.1145/321941.321956.
122. Hu, X., Tao, C.V., Prenzel, B., Automatic segmentation of high-resolution satellite imagery by integrating texture, intensity, and color features. Photogramm. Eng. Remote Sens., 71, 2005, 1399, 10.14358/PERS.71.12.1399.
123. Hu, Y., Chen, J., Pan, D., Hao, Z., Edge-guided image object detection in multiscale segmentation for high-resolution remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 54 (2016), 4702–4711, 10.1109/TGRS.2016.2550059.
124. Huang, Z., Zhang, J., Li, X., Zhang, H., Remote sensing image segmentation based on Dynamic Statistical Region Merging. Opt. – Int. J. Light Electron Opt. 125 (2014), 870–875, 10.1016/j.ijleo.2013.07.092.
125. Iannizzotto, G., Vita, L., Fast and accurate edge-based segmentation with no contour smoothing in 2-D real images. IEEE Trans. Image Process. 9 (2000), 1232–1237, 10.1109/83.847835.
126. Ikonomopoulos, A., An approach to edge detection based on the direction of edge elements. Comput. Graph. Image Process. 19 (1982), 179–195, 10.1016/0146-664X(82)90107-1.
127. Im, J., Quackenbush, L.J., Li, M., Fang, F., Optimum scale in object-based image analysis. Weng, Q., (eds.) Scale Issues in Remote Sensing, 2014, John Wiley & Sons Ltd., New Jersey, 197–214.
128. Jain, R., Kasturi, R., Schunck, B.G., Machine Vision. 1995, McGraw-Hill.
129. Jaing, J.-A., Chuang, C.-L., Lu, Y.-L., Fahn, C.-S., Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions. IET Image Process. 1 (1994), 269–277.
130. Jevtic, A., Melgar, I., Andina, D., 2009. Ant based edge linking algorithm. In: 35th Annual Conference of IEEE on Industrial Electronics, 2009. IEEE, pp. 3353–3358. https://doi.org/10.1109/IECON.2009.5415195.
131. Jiao, L., Gong, M., Wang, S., Hou, B., Zheng, Z., Wu, Q., Natural and remote sensing image segmentation using memetic computing. IEEE Comput. Intell. Mag. 5 (2010), 78–91, 10.1109/MCI.2010.936307.
132. Johnson, B., Bragais, M., Endo, I., Magcale-Macandog, D., Macandog, P., Image segmentation parameter optimization considering within- and between-segment heterogeneity at multiple scale levels: test case for mapping residential areas using landsat imagery. ISPRS Int. J. Geo-Information 4 (2015), 2292–2305, 10.3390/ijgi4042292.
133. Johnson, B., Jozdani, S., Identifying generalizable image segmentation parameters for urban land cover mapping through meta-analysis and regression tree modeling. Remote Sens., 10, 2018, 73, 10.3390/rs10010073.
134. Johnson, B., Xie, Z., Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens. 66 (2011), 473–483, 10.1016/j.isprsjprs.2011.02.006.
135. Johnson, B.A., High-resolution urban land-cover classification using a competitive multi-scale object-based approach. Remote Sens. Lett. 4 (2013), 131–140, 10.1080/2150704X.2012.705440.
136. Judah, A., Hu, B., Wang, J., An algorithm for boundary adjustment toward multi-scale adaptive segmentation of remotely sensed imagery. Remote Sens. 6 (2014), 3583–3610, 10.3390/rs6053583.
137. Juel, A., Groom, G.B., Svenning, J.-C., Ejrnæs, R., Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data. Int. J. Appl. Earth Obs. Geoinf. 42 (2015), 106–114, 10.1016/J.JAG.2015.05.008.
138. Jung, M., Yun, E.J., Kim, C.S., Multiresolution approach for texture segmentation using MRF models. International Geoscience and Remote Sensing Symposium (IGARSS), 2005, IEEE, 10.1109/IGARSS.2005.1525782.
139. Kaganami, H.G., Beiji, Z., Region-based segmentation versus edge detection. 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2009, IEEE, 1217–1221, 10.1109/IIH-MSP.2009.13.
140. Kalantar, B., Mansor, S. Bin, Sameen, M.I., Pradhan, B., Shafri, H.Z.M., Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis. Int. J. Remote Sens. 38 (2017), 2535–2556, 10.1080/01431161.2016.1277043.
141. Karl, J.W., Maurer, B.A., Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information. Ecol. Inform. 5 (2010), 194–202, 10.1016/j.ecoinf.2010.02.004.
142. Kass, M., Witkin, A., Terzopoulos, D., Snakes: Active contour models. Int. J. Comput. Vis. 1 (1988), 321–331, 10.1007/BF00133570.
143. Kaur, B., Garg, A., 2011. Mathematical morphological edge detection for remote sensing images. In: ICECT 2011 - 2011 3rd Int. Conf. Electron. Comput. Technol., vol. 5, pp. 324–327. https://doi.org/10.1109/ICECTECH.2011.5942012.
144. Kavzoglu, T., Erdemir, M.Y., A hierarchical scale setting strategy for improved segmentation performance using very high resolution images. Spatial Accuracy., 2016, 195–201.
145. Kavzoglu, T., Tonbul, H., 2017. A comparative study of segmentation quality for multi-resolution segmentation and watershed transform. In: Proc. 8th Int. Conf. Recent Adv. Sp. Technol. RAST 2017, pp. 113–117. https://doi.org/10.1109/RAST.2017.8002984.
146. Kavzoglu, T., Yildiz, E.M., Tonbul, H., A region-based multi-scale approach for object-based image analysis. ISPRS - Int. Arch. Photogramm. Remote Sens Spat. Inf. Sci., 41, 2016, 10.5194/isprs-archives-XLI-B7-241-2016.
147. Kelkar, D., Gupta, S., Improved Quadtree Method for Split Merge Image Segmentation. 2008 First International Conference on Emerging Trends in Engineering and TechnologyE, 2008, IEE, 44–47, 10.1109/ICETET.2008.145.
148. Kemker, R., Salvaggio, C., Kanan, C., Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning. ISPRS J. Photogramm. Remote Sens., 2018, 10.1016/j.isprsjprs.2018.04.014.
149. Kerem, S., Ulusoy, I., Automatic multi-scale segmentation of high spatial resolution satellite images using watersheds. Geoscience and Remote Sensing Symposium (IGARSS), 2013, IEEE International, 2505–2508.
150. Kerfoot, I.B., Bresler, Y., Theoretical analysis of multispectral image segmentation criteria. IEEE Trans. image Process. 8 (1999), 798–820, 10.1109/83.766858.
151. Kermad, C.D., Chehdi, K., Automatic image segmentation system through iterative edge-region co-operation. Image Vis. Comput. 20 (2002), 541–555, 10.1016/S0262-8856(02)00043-4.
152. Kim, M., Warner, T.A., Madden, M., Atkinson, D.S., Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects. Int. J. Remote Sens. 32 (2011), 2825–2850, 10.1080/01431161003745608.
153. Kiryati, N., Eldar, Y., A probabilistic hough transform. Pattern Recognit. 24 (1991), 303–316.
154. Ko, H.-Y., Ding, J.-J., Adaptive growing and merging algorithm for image segmentation. 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2016, IEEE, Jeju, South Korea, 10.1109/APSIPA.2016.7820762.
155. Körting, T.S., Castejon, E.F., Fonseca, L.M.G., The divide and segment method for parallel image segmentation. Advanced Concepts for Intelligent Vision Systems, 2013, Springer, 504–515, 10.1007/978-3-319-02895-8_45.
156. Krishnamachari, S., Chellappa, R., Multiresolution Gauss-Markov random field models for texture segmentation. IEEE Trans. Image Process. 6 (1997), 251–267, 10.1109/83.551696.
157. Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H., The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 44 (1993), 145–163, 10.1016/0034-4257(93)90013-N.
158. Kundu, M.K., Pal, S.K., Thresholding for edge detection using human psychovisual phenomena. Pattern Recognit. Lett. 4 (1986), 433–441, 10.1016/0167-8655(86)90041-3.
159. Kurnaz, M.N., Dokur, Z., Ölmez, T., Segmentation of remote-sensing images by incremental neural network. Pattern Recognit. Lett. 26 (2005), 1096–1104, 10.1016/J.PATREC.2004.10.004.
160. Laliberte, A.S., Rango, A., Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE Trans. Geosci. Remote Sens. 47 (2009), 1–10, 10.1109/TGRS.2008.2009355.
161. Lang, S., Object-based image analysis for remote sensing applications : modeling reality – dealing with complexity. Object-Based Image Analysis. Berlin, Heidelberg, 2008, 3–27.
162. Levinshtein, A., Stere, A., Kutulakos, K.N., Fleet, D.J., Dickinson, S.J., Siddiqi, K., TurboPixels: Fast superpixels using geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 31 (2009), 2290–2297, 10.1109/TPAMI.2009.96.
163. Levner, I., Zhang, H., Classification-driven watershed segmentation. IEEE Trans. Image Process. 16 (2007), 1437–1445, 10.1109/TIP.2007.894239.
164. Leymarie, F., Levine, M.D., Tracking deformable objects in the plane using an active contour model. IEEE Trans. Pattern Anal. Mach. Intell. 15 (1993), 617–634, 10.1109/34.216733.
165. Li, B., Pan, M., Wu, Z., An Improved Segmentation of High Spatial Resolution Remote Sensing Image using Marker-based Watershed Algorithm. Geoinformatics (GEOINFORMATICS), 2012 20th International Conference On, 2012, IEEE, 1–5.
166. Li, D., Zhang, G., Wu, Z., Yi, L., An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. IEEE Trans. Image Process. 19 (2010), 2781–2787, 10.1109/TIP.2010.2049528.
167. Li, H., Gu, H., Han, Y., Yang, J., Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. Int. J. Remote Sens. 31 (2010), 1453–1470, 10.1080/01431160903475266.
168. Li, H., Gu, H., Han, Y., Yang, J., An efficient multiscale SRMMHR (Statistical Region Merging and Minimum Heterogeneity Rule) segmentation method for high-resolution remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2 (2009), 67–73, 10.1109/JSTARS.2009.2022047.
169. Li, H.T., Gu, H.Y., Han, Y.S., Yang, J.S., An efficient multi-scale segmentation for highresolution remote sensing imagery based on Statistical region merging and minimum heterogeneity rule. Int. Work. Earth Obs. Remote Sens. Appl, 2008, 1257–1262.
170. Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D., A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. Int. J. Appl. Earth Obs. Geoinf. 49 (2016), 87–98, 10.1016/j.jag.2016.01.011.
171. Li, N., Huo, H., Fang, T., A novel texture-preceded segmentation algorithm for hr image.pdf. IEEE Trans. Geosci. Remote Sens. 48 (2010), 2818–2828.
172. Li, P., Guo, J., Song, B., Xiao, X., A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 4 (2011), 103–116, 10.1109/JSTARS.2010.2074186.
173. Li, X., Myint, S.W., Zhang, Y., Galletti, C., Zhang, X., Turner, B.L., Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography. Int. J. Appl. Earth Obs. Geoinf. 33 (2014), 321–330, 10.1016/J.JAG.2014.04.018.
174. Li, X., Shao, G., Object-based land-cover mapping with high resolution aerial photography at a county scale in midwestern USA. Remote Sens. 6 (2014), 11372–11390, 10.3390/rs61111372.
175. Li, X., Shao, G., Object-based urban vegetation mapping with high-resolution aerial photography as a single data source. Int. J. Remote Sens. 34 (2013), 771–789, 10.1080/01431161.2012.714508.
176. Lin, G., Adiga, U., Olson, K., Guzowski, J.F., Barnes, C.A., Roysam, B., A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks. Cytometry 56 (2003), 23–36, 10.1002/cyto.a.10079.
177. Ling, F., Li, X., Xiao, F., Fang, S., Dub, Y., Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery. Int. J. Appl. Earth Obs. Geoinf. 18 (2012), 283–292, 10.1016/j.jag.2012.02.008.
178. Liu, J., Li, P., Wang, X., A new segmentation method for very high resolution imagery using spectral and morphological information. ISPRS J. Photogramm. Remote Sens. 101 (2015), 145–162, 10.1016/j.isprsjprs.2014.11.009.
179. Liu, J., Tang, Z., Cui, Y., Wu, G., Local competition-based superpixel segmentation algorithm in remote sensing. Sensors, 17, 2017, 1364, 10.3390/s17061364.
180. Liu, L., A modified approach combining FNEA and watershed algorithms for segmenting remotely-sensed optical images, in: SPIE 10620. 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 2018, SPIE, Beijing, China, 10.1117/12.2300543.
181. Liu, L., Sclaroff, S., 2001. Region segmentation via deformable model-guided split and merge. In: Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. IEEE Comput. Soc, pp. 98–104. https://doi.org/10.1109/ICCV.2001.937504.
182. Liu, W., Liang, Y., Ren, X., Duan, P., A new contour detection in mammogram using sequential edge linking. 2008 Second International Symposium on Intelligent Information Technology Application, 2008, IEEE, 197–200, 10.1109/IITA.2008.410.
183. Liu, Y., Bian, L., Meng, Y., Wang, H., Zhang, S., Yang, Y., Shao, X., Wang, B., Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS J. Photogramm. Remote Sens. 68 (2012), 144–156, 10.1016/j.isprsjprs.2012.01.007.
184. Lizarazo, I., Elsner, P., 2011. Segmentation of remotely sensed imagery: moving from sharp objects to fuzzy regions. In: Ho, P.-G. (Ed.), Image Segmentation. InTech, pp. 249–272.
185. Lu, D.-S., Chen, C.-C., Edge detection improvement by ant colony optimization. Pattern Recognit. Lett. 29 (2008), 416–425, 10.1016/J.PATREC.2007.10.021.
186. Lu, Y., Jain, R.C., Behavior of edges in scale space. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 337–356, 10.1109/34.19032.
187. Lucchese, L., Mitray, S.K., 2001. Color image segmentation: A state-of-the-art survey. In: Indian National Science Academy (INSA-A). Delhi, India, pp. 207–221. https://doi.org/10.1.1.84.4896.
188. Lucieer, A., Stein, A., Fisher, P., Multivariate texture-based segmentation of remotely sensed imagery for extraction of objects and their uncertainty. Int. J. Remote Sens. 26 (2005), 2917–2936, 10.1080/01431160500057723.
189. Ma, L., Cheng, L., Li, M., Liu, Y., Ma, X., Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS J. Photogramm. Remote Sens. 102 (2015), 14–27, 10.1016/j.isprsjprs.2014.12.026.
190. Ma, L., Li, M., Ma, X., Cheng, L., Du, P., Liu, Y., A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote Sens. 130 (2017), 277–293, 10.1016/j.isprsjprs.2017.06.001.
191. Maboudi, M., Amini, J., Hahn, M., Objects grouping for segmentation of roads network in high resolution images of urban areas. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch. 41 (2016), 897–902, 10.5194/isprsarchives-XLI-B7-897-2016.
192. Maboudi, M., Amini, J., Hahn, M., Saati, M., Road network extraction from VHR satellite images using context aware object feature integration and tensor voting. Remote Sens., 8, 2016, 10.3390/rs8080637.
193. Maintz, T., Segmentation. Digital and Medical Image Processing, 2005, Universiteit Utrecht.
194. Malenovský, Z., Bartholomeus, H.M., Acerbi-Junior, F.W., Schopfer, J.T., Painter, T.H., Epema, G.F., Bregt, A.K., Scaling dimensions in spectroscopy of soil and vegetation. Int. J. Appl. Earth Obs. Geoinf. 9 (2007), 137–164, 10.1016/J.JAG.2006.08.003.
195. Mallinis, G., Koutsias, N., Tsakiri-Strati, M., Karteris, M., Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS J. Photogramm. Remote Sens. 63 (2008), 237–250, 10.1016/j.isprsjprs.2007.08.007.
196. Manjunath, B.S., Simchony, T., Chellappa, R., Stochastic and deterministic networks for texture segmentation. IEEE Trans. Acoust. 38 (1990), 1039–1049, 10.1109/29.56064.
197. Manousakas, I.N., Undrill, P.E., Cameron, G.G., Redpath, T.W., Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comput. Biomed. Res. 31 (1998), 393–412, 10.1006/CBMR.1998.1489.
198. Marceau, D.J., The scale issue in the social and natural sciences. Can. J. Remote Sens. 25 (1999), 347–356, 10.1080/07038992.1999.10874734.
199. Mardia, K.V., Hainsworth, T.J., A spatial thresholding method for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 10 (1988), 919–927, 10.1109/IVS.2005.1505186.
200. Marpu, P.R., Neubert, M., Herold, H., Niemeyer, I., Enhanced evaluation of image segmentation results. J. Spat. Sci. 55 (2010), 55–68, 10.1080/14498596.2010.487850.
201. Marr, D., Hildreth, E., Theory of edge detection. Proc. R. Soc. London Ser. BBiol. Sci. 207 (1980), 187–217, 10.1098/RSPB.1980.0020.
202. Martha, T.R., Kerle, N., Van Westen, C.J., Jetten, V., Kumar, K.V., Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sens. 49 (2011), 4928–4943, 10.1109/TGRS.2011.2151866.
203. Martin, D.R., Fowlkes, C.C., Malik, J., Learning to detect natural image boundaries using local brightness and texture cues. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004), 1–20, 10.1109/TPAMI.2004.1273918.
204. Mathieu, R., Aryal, J., Chong, A.k., Object-based classification of ikonos imagery for mapping large-scale vegetation communities in urban areas. Sensors 7 (2007), 2860–2880, 10.3390/s7112860.
205. Maxwell, T., Zhang, Y., A Fuzzy Logic Approach To Supervised Segmentation For Object Oriented Classification. ASPRS Annual Conference Reno. Nevada, 2006, 1–5.
206. Mayunga, S.D., Coleman, D.J., Zhang, Y., A semi-automated approach for extracting buildings from QuickBird imagery applied to informal settlement mapping. Int. J. Remote Sens. 28 (2007), 2343–2357, 10.1080/01431160600868474.
207. Meer, P., Georgescu, B., Edge detection with embedded confidence. IEEE Trans. Pattern Anal. Mach. Intell. 23 (2001), 1351–1365, 10.1109/34.977560.
208. Mehnert, A., Jackway, P., An improved seeded region growing algorithm. Pattern Recognit. Lett. 18 (1997), 1065–1071, 10.1016/S0167-8655(97)00131-1.
209. Meinel, G., Neubert, M., Sensing, R., City, L., A comparison of segmentation programs for high resolution remote sensing data. Int. Arch. Photogramm. Remote Sens. 35 (2004), 1097–1105.
210. Melas, D.E., Wilson, S.P., Double Markov random fields and Bayesian image segmentation. IEEE Trans. Signal Process. 50 (2002), 357–365, 10.1109/78.978390.
211. Meng, Y., Lin, C., Cui, W., Yao, J., Scale selection based on Moran's I for segmentation of high resolution remotely sensed images. IEEE Int. Geosci. Remote Sens. Symp., 2014, 4895–4898.
212. Meyer, F., Beucher, S., Morphological segmentation. J. Vis. Commun. Image Represent. 1 (1990), 21–46, 10.1016/1047-3203(90)90014-M.
213. Mezaris, V., Kompatsiaris, I., Strintzis, M.G., Still image segmentation tools for object-based multimedia applications. Int. J. Pattern Recognit. Artif. Intell. 18 (2004), 701–725, 10.1142/S0218001404003393.
214. Miao, Z., Shi, W., Gamba, P., Li, Z., An object-based method for road network extraction in vhr satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 4853–4862, 10.1109/JSTARS.2015.2443552.
215. Miao, Z., Shi, W., Zhang, H., Wang, X., Road centerline extraction from high-resolution imagery based on shape features and multivariate adaptive regression splines. IEEE Geosci. Remote Sens. Lett. 10 (2013), 583–587, 10.1109/LGRS.2012.2214761.
216. Michel, J., Grizonnet, M., Jaen, A., Harasse, S., Hermitte, L., 2012. Open tools and methods for large scale segmentation of very high resolution satellite images. In: OGRS. pp. 179–184.
217. Michel, J., Youssefi, D., Grizonnet, M., Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images. IEEE Trans. Geosci. Remote Sens. 53 (2015), 952–964, 10.1109/TGRS.2014.2330857.
218. Ming, D., Ci, T., Cai, H., Li, L., Qiao, C., Du, J., Semivariogram-based spatial bandwidth selection for remote sensing image segmentation with mean-shift algorithm. IEEE Geosci. Remote Sens. Lett. 9 (2012), 813–817, 10.1109/LGRS.2011.2182604.
219. Ming, D., Li, J., Wang, J., Zhang, M., Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example. ISPRS J. Photogramm. Remote Sens. 106 (2015), 28–41, 10.1016/j.isprsjprs.2015.04.010.
220. Mirghasemi, S., Rayudu, R., Zhang, M., A new image segmentation algorithm based on modified seeded region growing and particle swarp optimization. Image Vis. Comput. 28 (2013), 382–387.
221. Mitra, P., Uma Shankar, B., Pal, S.K., Segmentation of multispectral remote sensing images using active support vector machines. Pattern Recognit. Lett. 25 (2004), 1067–1074, 10.1016/J.PATREC.2004.03.004.
222. Moellering, H., Tobler, W., Geographical variances. Geogr. Anal. 4 (1972), 34–50, 10.1111/j.1538-4632.1972.tb00455.x.
223. Mohammadzadeh, A., Zoej, M.J.V., A Self-organizing Fuzzy Segmentation (SOFS) Method for Road Detection from High Resolution Satellite Images. Photogramm. Eng. Remote Sens. 76 (2010), 27–35 https://doi.org/10.14358/PERS.76.1.27.
224. Moigne, J. Le, Tilton, J.C., Member, S., Refining image segmentation. IEEE Trans. Geosci. Remote Sens., 33, 1995.
225. Mokhtarzade, M., Zoej, M.J.V., Road detection from high-resolution satellite images using artificial neural networks. Int. J. Appl. Earth Obs. Geoinf. 9 (2007), 32–40, 10.1016/j.jag.2006.05.001.
226. Montaghi, A., Larsen, R., Greve, M.H., Accuracy assessment measures for image segmentation goodness of the Land Parcel Identification System (LPIS) in Denmark. Remote Sens. Lett. 4 (2013), 946–955, 10.1080/2150704X.2013.817709.
227. Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., Jones, G., Superpixel lattices. IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008, IEEE, 1–8., 10.1109/CVPR.2008.4587471.
228. Moser, G., Serpico, S.B., Classification of high-resolution images based on MRF fusion and multiscale segmentation. International Geoscience and Remote Sensing Symposium (IGARSS), 2008, IEEE, 277–280, 10.1109/IGARSS.2008.4778981.
229. Moser, G., Serpico, S.B., Benediktsson, J.A., Land-cover mapping by markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proc. IEEE 101 (2013), 631–651, 10.1109/JPROC.2012.2211551.
230. Mountrakis, G., Im, J., Ogole, C., Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 66 (2011), 247–259, 10.1016/j.isprsjprs.2010.11.001.
231. Mueller, M., Segl, K., Kaufmann, H., Edge-and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery. Pattern Recognit. 37 (2004), 1619–1628, 10.1016/j.patcog.2004.03.001.
232. Munoz, X., Freixenet, J., Cufi, X., Marti, J., Strategies for image segmentation combining region and boundary information. Pattern Recognit. Lett. 24 (2003), 375–392.
233. Mylonas, S.K., Stavrakoudis, D.G., Theocharis, J.B., Mastorocostas, P.A., A region-based GeneSIS segmentation algorithm for the classification of remotely sensed images. Remote Sens. 7 (2015), 2474–2508, 10.3390/rs70302474.
234. Najman, L., Schmitt, M., Geodesic saliency of watershed contours and hierarchical segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 18 (1996), 1163–1173, 10.1109/34.546254.
235. Natowicz, R., Bergen, L., Gas, B., 1995. Kohonen's Maps for Contour and “Region-Like” Segmentation of Gray Level and Color Images. In: Artificial Neural Nets and Genetic Algorithms. Springer Vienna, Vienna, pp. 360–363. https://doi.org/10.1007/978-3-7091-7535-4_94.
236. Neteler, M., Beaudette, D.E., Cavallini, P., Lami, L., Cepicky, J., GRASS GIS. Balram, S., Dragicevic, S., (eds.) Open Source Approaches in Spatial Data Handling, 2008, Springer Berlin Heidelberg, Berlin, Heidelberg, 171–199, 10.1007/978-3-540-74831-1_9.
237. Neubert, M., Herold, H., Meinel, G., 2008. Assessing image segmentation quality – concepts, methods and application. In: Blaschke, T., S.L. and G.H. (Ed.), Object-Based Image Analysis – Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Springer, Berlin, pp. 769–784.
238. Neubert, P., Protzel, P., Superpixel benchmark and comparison. Forum Bildverarbeitung, 2012, Karlsruher Instituts für Technologie (KIT) Scientific Publishing, Karlsruhe, Germany, 1–12.
239. Nichol, J., Wong, M.S., Habitat mapping in rugged terrain using multispectral ikonos images. Photogramm. Eng. Remote Sens. 74 (2008), 1325–1334 https://doi.org/10.14358/PERS.74.11.1325.
240. Nielsen, F., Nock, R., On Region Merging: The Statistical Soundness of Fast Sorting, with Applications. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, IEEE, Madison, WI, USA, 10.1109/CVPR.2003.1211447.
241. Nigam, I., Huang, C., Ramanan, D., Ensemble knowledge transfer for semantic segmentation. 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 2018, IEEE, Lake Tahoe, NV, USA, 1499–1508, 10.1109/WACV.2018.00168.
242. Nijland, W., Addink, E.A., De Jong, S.M., Van der Meer, F.D., Optimizing spatial image support for quantitative mapping of natural vegetation. Remote Sens. Environ. 113 (2009), 771–780, 10.1016/J.RSE.2008.12.002.
243. Nikfar, M., Zoej, M.J.V., Mohammadzadeh, A., Mokhtarzade, M., Navabi, A., Optimization of multiresolution segmentation by using a genetic algorithm. J. Appl. Remote Sens., 6, 2012, 063592, 10.1117/1.JRS.6.063592.
244. Ninsawat, S., Hossain, M.D., Identifying potential area and financial prospects of rooftop solar photovoltaics (PV). Sustainability, 8, 2016, 1068, 10.3390/su8101068.
245. Nock, R., Nielsen, F., Statistical region merging. IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004), 1452–1458.
246. Ohlander, R., Price, K., Reddy, D.R., Picture segmentation using a recursive region splitting method. Comput. Graph. Image Process. 8 (1978), 313–333, 10.1016/0146-664X(78)90060-6.
247. Ohta, Y.-I., Kanade, T., Sakai, T., Color information for region segmentation. Comput. Graph. Image Process. 13 (1980), 222–241, 10.1016/0146-664X(80)90047-7.
248. Ojala, T., Pietikäinen, M., Unsupervised texture segmentation using feature distributions. Pattern Recognit. 32 (1999), 477–486, 10.1016/S0031-3203(98)00038-7.
249. Opitz, D., Blundell, S., Object recognition and image segmentation: the Feature Analyst® approach. Blaschke, T., Lang, S., Hay, G.J., (eds.) Object-Based Image Analysis, 2008, Springer, Berlin, Heidelberg, Berlin, Heidelberg, 153–167, 10.1007/978-3-540-77058-9_8.
250. Pal, N.R., Pal, S.K., A review on image segmentation techniques. Pattern Recognit. 26 (1993), 1277–1294, 10.1016/0031-3203(93)90135-J.
251. Pal, S.K., Pal, N.R., Segmentation based on measures of contrast, homogeneity, and region size. IEEE Trans. Syst. Man. Cybern. 17 (1987), 857–868, 10.1109/TSMC.1987.6499294.
252. Peña-Barragán, J.M., Ngugi, M.K., Plant, R.E., Six, J., Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sens. Environ. 115 (2011), 1301–1316, 10.1016/j.rse.2011.01.009.
253. Peng, J., Zhang, D., Liu, Y., An improved snake model for building detection from urban aerial images. Pattern Recognit. Lett. 26 (2005), 587–595, 10.1016/j.patrec.2004.09.033.
254. Percival, D.P., On estimation of the wavelet variance. Biometrika 82 (1995), 619–631, 10.1093/biomet/82.3.619.
255. Perona, P., Malik, J., Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell. 12 (1990), 629–639, 10.1109/34.56205.
256. Pesaresi, M., Benediktsson, J.A., A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 39 (2001), 309–320, 10.1109/36.905239.
257. Pham, D.L., Xu, C., Prince, J.L., Current methods in medical image segmentation. Annu. Rev. Biomed. Eng. 2 (2000), 315–337.
258. Poggi, G., Scarpa, G., Zerubia, J.B., Supervised segmentation of remote sensing images based on a tree-structured MRF model. IEEE Trans. Geosci. Remote Sens. 43 (2005), 1901–1911, 10.1109/TGRS.2005.852163.
259. Pong, T.-C., Shapiro, L.G., Watson, L.T., Haralick, R.M., Experiments in segmentation using a facet model region grower. Comput. Vision Graph. Image Process. 25 (1984), 1–23, 10.1016/0734-189X(84)90046-X.
260. Poullis, C., You, S., Delineation and geometric modeling of road networks. ISPRS J. Photogramm. Remote Sens. 65 (2010), 165–181, 10.1016/J.ISPRSJPRS.2009.10.004.
261. Prewitt, J.M.S., Picture Processing and Psychopictorics. 1970, Elsevier Science, New York.
262. Pu, R., Landry, S., A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species. Remote Sens. Environ. 124 (2012), 516–533, 10.1016/j.rse.2012.06.011.
263. Pu, R., Landry, S., Yu, Q., Object-based urban detailed land cover classification with high spatial resolution IKONOS imagery. Int. J. Remote Sens. 32 (2011), 3285–3308, 10.1080/01431161003745657.
264. Qiu, Y., Ming, D., Zhang, X., Object oriented land cover classification combining scale parameter preestimation and mean-shift segmentation. International Geoscience and Remote Sensing Symposium (IGARSS), 2016, IEEE, Beijing, China, 6332–6335, 10.1109/IGARSS.2016.7730655.
265. Quirita, V.A.A., Diaz, P.A., Feitosa, R.Q., Happ, P.N., Costa, G.A.O.P., Klinger, T., Heipke, C., Metaheuristics for supervised parameter tuning of multiresolution segmentation. IEEE Geosci. Remote Sens. Lett. 13 (2016), 1364–1368, 10.1109/LGRS.2016.2586499.
266. Radoux, J., Defourny, P., A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery. Remote Sens. Environ. 110 (2007), 468–475, 10.1016/J.RSE.2007.02.031.
267. Rashedi, E., Nezamabadi-pour, H., A stochastic gravitational approach to feature based color image segmentation. Eng. Appl. Artif. Intell. 26 (2013), 1322–1332, 10.1016/J.ENGAPPAI.2012.10.002.
268. Ren, X., Malik, J., Learning a classification model for segmentation. Ninth IEEE International Conference on Computer Vision (ICCV). Marseille, France, 2003, 10–17, 10.1109/ICCV.2003.1238308.
269. Robinson, G.S., Edge detection by compass gradient masks. Comput. Graph. Image Process. 6 (1977), 492–501, 10.1016/S0146-664X(77)80024-5.
270. Rosenfield, A., Davis, L., Image segmentation and image model. Proc. IEEE 67 (1979), 764–772.
271. Saba, F., Zoej, M.J.V., Mokhtarzade, M., Optimization of Multiresolution Segmentation for Object-Oriented Road Detection from High-Resolution Images. Can. J. Remote Sens. 42 (2016), 75–84, 10.1080/07038992.2016.1160770.
272. Sahr, K., White, D., Kimerling, A.J., Geodesic Discrete Global Grid Systems. Cartogr. Geogr. Inf. Sci. 30 (2003), 121–134, 10.1559/152304003100011090.
273. Sappa, A.D., Unsupervised contour closure algorithm for range image edge-based segmentation. IEEE Trans. Image Process. 15 (2006), 377–384, 10.1109/TIP.2005.860612.
274. Sarkar, A., Biswas, M.K., Sharma, K.M., A Simple Unsupervised MRF Model Based Image Segmentation Approach. IEEE Trans. Image Process. 9 (2000), 801–812.
275. Schiewe, J., Segmentation of high-resolution remotely sensed data-concepts, applications and problems. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34 (2002), 380–385.
276. Shackelford, A.K., Davis, C.H., A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas. IEEE Trans. Geosci. Remote Sens. 41 (2003), 2354–2363, 10.1109/TGRS.2003.815972.
277. Shankar, B., Novel classification and segmentation techniques with application to remotely sensed images. Trans. rough sets VII, 2007, 295–380.
278. Shi, C., Wang, L., Incorporating spatial information in spectral unmixing: A review. Remote Sens. Environ. 149 (2014), 70–87, 10.1016/J.RSE.2014.03.034.
279. Shi, W., Miao, Z., Debayle, J., An integrated method for urban main-road centerline extraction from optical remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 52 (2014), 3359–3372, 10.1109/TGRS.2013.2272593.
280. Shih, F.Y., Cheng, S., Automatic seeded region growing for color image segmentation. Image Vis. Comput. 23 (2005), 877–886, 10.1016/j.imavis.2005.05.015.
281. Shih, F.Y., Cheng, S., Adaptive mathematical morphology for edge linking. Inf. Sci. (Ny) 167 (2004), 9–21, 10.1016/j.ins.2003.07.020.
282. Srivastava, M., Arora, M.K., Raman, B., Selection of critical segmentation-A prerequisite for Object based image classification. National Conference on Recent Advances in Electronics & Computer Engineering, 2015, IEEE, Roorkee, India, 143–148, 10.1109/RAECE.2015.7510243.
283. Stefanski, J., MacK, B., Waske, O., Optimization of object-based image analysis with random forests for land cover mapping. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 6 (2013), 2492–2504, 10.1109/JSTARS.2013.2253089.
284. Stumpf, A., Kerle, N., Object-oriented mapping of landslides using Random Forests. Remote Sens. Environ. 115 (2011), 2564–2577, 10.1016/j.rse.2011.05.013.
285. Su, T., A novel region-merging approach guided by priority for high resolution image segmentation. Remote Sens. Lett. 8 (2017), 771–780, 10.1080/2150704X.2017.1320441.
286. Su, T., Zhang, S., Multi-Scale Segmentation Method Based on Binary Merge Tree and Class Label Information. IEEE Access 6 (2018), 17801–17816, 10.1109/ACCESS.2018.2819988.
287. Su, T., Zhang, S., Local and global evaluation for remote sensing image segmentation. ISPRS J. Photogramm. Remote Sens. 130 (2017), 256–276, 10.1016/j.isprsjprs.2017.06.003.
288. Suk, M., Chung, S.-M., A new image segmentation technique based on partition mode test. Pattern Recognit. 16 (1983), 469–480, 10.1016/0031-3203(83)90051-1.
289. Sun, W., Messinger, D.W., Knowledge-based automated road network extraction system using multispectral images. Opt. Eng., 52, 2013, 047203, 10.1117/1.OE.52.4.047203.
290. Sun, Y., He, G.J., Segmentation of high-resolution remote sensing image based on marker-based watershed algorithm. 5th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2008, 2008, IEEE, 271–276, 10.1109/FSKD.2008.249.
291. Svoboda, T., Jan, K., Vaclav, H., 2007. Image Processing, Analysis & Machine Vision - A Matlab Companion. Thomas Learning.
292. Tarabalka, Y., Chanussot, J., Benediktsson, J.A., Angulo, J., Fauvel, M., Segmentation and Classification of Hyperspectral Data using Watershed. International Geoscience and Remote Sensing Symposium (IGARSS), 2008, IEEE International, 652–655, 10.1109/IGARSS.2008.4779432.
293. Tehrany, M.S., Pradhan, B., Jebuv, M.N., A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto Int. 29 (2014), 351–369, 10.1080/10106049.2013.768300.
294. Teodoro, A.C., Araujo, R., Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data. J. Appl. Remote Sens., 10, 2016, 016011, 10.1117/1.JRS.10.016011.
295. Tilton, J.C., 2010. Split-remerge method for eliminating processing window artifacts in recursive hierarchical segmentation. 7,697,759.
296. Tilton, J.C., Hall, D.K., Riggs, G.A., Creation of ersatz ground reference data for validating the MODIS snow and ICE product suite. International Geoscience and Remote Sensing Symposium (IGARSS), 2010, IEEE, Honolulu, HI, USA, 2371–2374, 10.1109/IGARSS.2010.5650287.
297. Tilton, J.C., Tarabalka, Y., Montesano, P.M., Gofman, E., Best merge region-growing segmentation with integrated nonadjacent region object aggregation. IEEE Trans. Geosci. Remote Sens. 50 (2012), 4454–4467, 10.1109/TGRS.2012.2190079.
298. Tran, T.N., Wehrens, R., Hoekman, D.H., Buydens, L.M.C., Initialization of Markov random field clustering of large remote sensing images. IEEE Trans. Geosci. Remote Sens. 43 (2005), 1912–1919, 10.1109/TGRS.2005.848427.
299. Tremeau, A., Borel, N., A region growing and merging algorithm to color segmentation. Pattern Recognit. 30 (1997), 1191–1203, 10.1016/S0031-3203(96)00147-1.
300. Trivedi, M.M., Bezdek, J.C., Low-level segmentation of aerial images with fuzzy clustering. IEEE Trans. Syst. Man. Cybern. 16 (1986), 589–598, 10.1109/TSMC.1986.289264.
301. Troya-Galvis, A., Gançarski, P., Berti-Équille, L., Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. Pattern Recognit. 73 (2018), 259–274, 10.1016/j.patcog.2017.08.030.
302. Troya-Galvis, A., Gançarski, P., Berti-Équille, L., Collaborative segmentation and classification for remote sensing image analysis. 23rd International Conference on Pattern Recognition (ICPR). IEEE, Cancun, Mexico, 2016, 829–834, 10.1109/ICPR.2016.7899738.
303. Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W.E., Willsky, A., A shape-based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22 (2003), 137–154, 10.1109/TMI.2002.808355.
304. Tupin, F., Roux, M., Markov random field on region adjacency graph for the fusion of SAR and optical data in radargrammetric applications. IEEE Trans. Geosci. Remote Sens. 43 (2005), 1920–1928, 10.1109/TGRS.2005.852080.
305. Turker, M., Sumer, E., Building-based damage detection due to earthquake using the watershed segmentation of the post-event aerial images. Int. J. Remote Sens. 29 (2008), 3073–3089, 10.1080/01431160701442096.
306. Tzotsos, A., Argialas, D., Mseg: a Generic Region-Based Multi-Scale Image Segmentation. Proceedings of ASPRS 2006 Annual Conference. Reno, Nevada, 2006.
307. Tzotsos, A., Karantzalos, K., Argialas, D., Object-based image analysis through nonlinear scale-space filtering. ISPRS J. Photogramm. Remote Sens. 66 (2011), 2–16, 10.1016/j.isprsjprs.2010.07.001.
308. Vedaldi, A., Soatto, S., 2008. Quick shift and kernel methods for mode seeking. In: Forsyth, D., Torr, P., Zisserman A. (Eds.), European Conference on Computer Vision – ECCV 2008. Springer, Berlin, Heidelberg, Berlin, Heidelberg, pp. 705–718. https://doi.org/10.1007/978-3-540-88693-8_52.
309. Verma, O.P., Hanmandlu, M., Susan, S., Kulkarni, M., Jain, P.K., 2011. A simple single seeded region growing algorithm for color image segmentation using adaptive thresholding. In: 2011 International Conference on Communication Systems and Network Technologies. IEEE, Jammu, India, pp. 500–503. https://doi.org/10.1109/CSNT.2011.107.
310. Villmann, T., Merényi, E., Hammer, B., Neural maps in remote sensing image analysis. Neural Networks 16 (2003), 389–403, 10.1016/S0893-6080(03)00021-2.
311. Vincent, L., Soille, P., Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 13 (1991), 583–598, 10.1109/34.87344.
312. Wang, C., Xu, W., Pei, X.F., Zhou, X.Y., An unsupervised multi-scale segmentation method based on automated parameterization. Arab. J. Geosci., 9, 2016, 10.1007/s12517-016-2683-4.
313. Wang, D., A multiscale gradient algorithm for image segmentation using watershelds. Pattern Recognit. 30 (1997), 2043–2052, 10.1016/S0031-3203(97)00015-0.
314. Wang, J., Aldred, D.a., A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images. Int. J. Remote Sens. 32 (2011), 2811–2823, 10.1080/01431161003745590.
315. Wang, L., Dai, Q., Hong, L., Liu, G., Adaptive regional feature extraction for very high spatial resolution image classification. J. Appl. Remote Sens., 6, 2012, 063506, 10.1117/1.JRS.6.063506.
316. Wang, L., Dai, Q., Xu, Q., Zhang, Y., Constructing hierarchical segmentation tree for feature extraction and land cover classification of high resolution MS imagery. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 1946–1961, 10.1109/JSTARS.2015.2428232.
317. Wang, M., Cui, Q., Wang, J., Ming, D., Lv, G., Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features. ISPRS J. Photogramm. Remote Sens. 123 (2017), 104–113, 10.1016/J.ISPRSJPRS.2016.10.008.
318. Wang, M., Dong, Z., Cheng, Y., Li, D., Optimal segmentation of high-resolution remote sensing image by combining superpixels with the minimum spanning tree. IEEE Trans. Geosci. Remote Sens. 56 (2017), 228–238, 10.1109/TGRS.2017.2745507.
319. Wang, M., Huang, J., Ming, D., Region-line association constraints for high-resolution image segmentation. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 10 (2017), 628–637.
320. Wang, M., Li, R., Segmentation of high spatial resolution remote sensing imagery based on hard-boundary constraint and two-stage merging. IEEE Trans. Geosci. Remote Sens. 52 (2014), 5712–5725, 10.1109/TGRS.2013.2292053.
321. Wang, M., Sun, Y., Chen, G., Refining high spatial resolution remote sensing image segmentation for man-made objects through a collinear and ipsilateral neighborhood model. Photogramm. Eng. Remote Sens. 81 (2015), 397–406 https://doi.org/10.14358/PERS.81.5.397.
322. Wang, M., Wan, Q.M., Gu, L.B., Song, T.Y., Remote-sensing image retrieval by combining image visual and semantic features. Int. J. Remote Sens. 34 (2013), 4200–4223, 10.1080/01431161.2013.774098.
323. Wang, M., Wang, J., A region-line primitive association framework for object-based remote sensing image analysis. Photogramm. Eng. Remote Sens. 82 (2016), 149–159 https://doi.org/10.14358/PERS.82.2.149.
324. Wang, P., Sun, G., Wang, Z., 2015. Seismic remote sensing image segmentation based on spectral histogram and dynamic region merging. In: Liu, J., Sun, H. (Eds.), Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015) - The International Society for Optical Engineering. SPIE. https://doi.org/10.1117/12.2209431.
325. Wang, Y., Meng, Q., Qi, Q., Yang, J., Liu, Y., Region merging considering within- and between-segment heterogeneity: an improved hybrid remote-sensing image segmentation method. Remote Sens., 10, 2018, 781, 10.3390/rs10050781.
326. Wang, Z., Jensen, J.R., Im, J., An automatic region-based image segmentation algorithm for remote sensing applications. Environ. Model. Softw. 25 (2010), 1149–1165, 10.1016/j.envsoft.2010.03.019.
327. Wang, Z., Lu, C., Yang, X., Exponentially sampling scale parameters for the efficient segmentation of remote-sensing images. Int. J. Remote Sens. 39 (2018), 1628–1654, 10.1080/01431161.2017.1410297.
328. Wang, Z., Song, C., Wu, Z., Chen, X., Improved Watershed Segmentation Algorithm for High Resolution Remote Sensing Images Using Texture. International Geoscience and Remote Sensing Symposium (IGARSS), 2005, IEEE International, 3721–3723.
329. Wang, Z., Yang, X., Lu, C., Yang, F., A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images. Int. J. Appl. Earth Obs. Geoinf. 69 (2018), 88–98, 10.1016/J.JAG.2018.03.001.
330. Weickert, J., Efficient image segmentation using partial differential equations and morphology. Pattern Recognit. 34 (2001), 1813–1824, 10.1016/S0031-3203(00)00109-6.
331. Witharana, C., Civco, D.L., Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2). ISPRS J. Photogramm. Remote Sens. 87 (2014), 108–121, 10.1016/J.ISPRSJPRS.2013.11.006.
332. Woodcock, C.E., Strahler, A.H., The factor of scale in remote sensing. Remote Sens. Environ. 21 (1987), 311–332, 10.1016/0034-4257(87)90015-0.
333. Wu, H., Li, Z.L., Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors 9 (2009), 1768–1793, 10.3390/s90301768.
334. Wu, L., Wang, Y., Long, J., Liu, Z., 2015. A non-seed-based region growing algorithm for high resolution remote sensing image segmentation. In: YJ, Z. (Ed.), Image and Graphics. ICIG 2015. Lecture Notes in Computer Science. Springer, pp. 263–277.
335. Wuest, B., Zhang, Y., Region based segmentation of QuickBird multispectral imagery through band ratios and fuzzy comparison. ISPRS J. Photogramm. Remote Sens. 64 (2009), 55–64.
336. Xiaohan, Y.X.Y., Yla-Jaaski, J., Huttunen, O., Vehkomaki, T., Sipila, O., Katila, T., Image segmentation combining region growing and edge detection. 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis, 1992, IEEE, 10.1109/ICPR.1992.202029.
337. Xing, J., Sieber, R., Kalacska, M., The challenges of image segmentation in big remotely sensed imagery data. Ann. GIS 20 (2014), 233–244, 10.1080/19475683.2014.938774.
338. Yang, J., He, Y., Caspersen, J., Region merging using local spectral angle thresholds: A more accurate method for hybrid segmentation of remote sensing images. Remote Sens. Environ. 190 (2017), 137–148, 10.1016/j.rse.2016.12.011.
339. Yang, J., He, Y., Caspersen, J., A self-adapted threshold-based region merging method for remote sensing image segmentation. International Geoscience and Remote Sensing Symposium (IGARSS), 2016, IEEE, Beijing, China, 6320–6323.
340. Yang, J., He, Y., Caspersen, J., Jones, T., A discrepancy measure for segmentation evaluation from the perspective of object recognition. ISPRS J. Photogramm. Remote Sens. 101 (2015), 186–192, 10.1016/j.isprsjprs.2014.12.015.
341. Yang, J., He, Y., Weng, Q., An automated method to parameterize segmentation scale by enhancing intrasegment homogeneity and intersegment heterogeneity. IEEE Geosci. Remote Sens. Lett. 12 (2015), 1282–1286, 10.1109/LGRS.2015.2393255.
342. Yang, J., Jones, T., Caspersen, J., He, Y., Object-based canopy gap segmentation and classification: Quantifying the pros and cons of integrating optical and LiDAR data. Remote Sens. 7 (2015), 15917–15932, 10.3390/rs71215811.
343. Yang, J., Li, P., He, Y., A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation. ISPRS J. Photogramm. Remote Sens. 94 (2014), 13–24, 10.1016/j.isprsjprs.2014.04.008.
344. Yang, Y., Han, C., Han, D., A Markov random field model-based fusion approach to segmentation of SAR and optical images. International Geoscience and Remote Sensing Symposium (IGARSS), 2008, IEEE, 802–805, 10.1109/IGARSS.2008.4779844.
345. Yin, D., Du, S., Wang, S., Guo, Z., A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8 (2015), 4785–4794, 10.1109/JSTARS.2015.2477097.
346. Yin, W., Yang, J., Sub-pixel vs. super-pixel-based greenspace mapping along the urban–rural gradient using high spatial resolution Gaofen-2 satellite imagery: a case study of Haidian District, Beijing China. Int. J. Remote Sens. 38 (2017), 6386–6406, 10.1080/01431161.2017.1354266.
347. Yu, Q., Clausi, D.A., IRGS: Image segmentation using edge penalties and region growing. IEEE Trans. Pattern Anal. Mach. Intell. 30 (2008), 2126–2139, 10.1109/TPAMI.2008.15.
348. Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D., Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sens. 72 (2006), 799–811 https://doi.org/10.14358/PERS.72.7.799.
349. Yuan, J., Cheriyadat, A.M., Road Segmentation in Aerial Images by Exploiting Road Vector Data. 2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013, IEEE, San Jose, CA, USA, 16–23, 10.1109/COMGEO.2013.4.
350. Yuan, J., Wang, D., Li, R., Remote sensing image segmentation by combining spectral and texture features. IEEE Trans. Geosci. Remote Sens. 52 (2014), 16–24, 10.1109/TGRS.2012.2234755.
351. Yue, A., Yang, J., Zhang, C., Su, W., Yun, W., Zhang, Y., A watershed segmentation method with shape-based merging criterion based on multispectral remote sensing imagery. Sens. Lett., 9, 2011, 10.1166/sl.2011.1389.
352. Zarrinpanjeh, N., Samadzadegan, F., Schenk, T., A new ant based distributed framework for urban road map updating from high resolution satellite imagery. Comput. Geosci. 54 (2013), 337–350, 10.1016/J.CAGEO.2012.12.006.
353. Zhang, A.Z., Sun, G.Y., Liu, S.H., Wang, Z.J., Wang, P., Ma, J.S., Multi-scale segmentation of very high resolution remote sensing image based on gravitational field and optimized region merging. Multimed. Tools Appl. 76 (2017), 15105–15122, 10.1007/s11042-017-4558-4.
354. Zhang, G., Jia, X., Hu, J., Superpixel-based graphical model for remote sensing image mapping. IEEE Trans. Geosci. Remote Sens. 53 (2015), 5861–5871, 10.1109/TGRS.2015.2423688.
355. Zhang, J., Tang, Z., Gui, W., Chen, Q., Liu, J., Interactive image segmentation with a regression based ensemble learning paradigm. Front. Inf. Technol. Electron. Eng. 18 (2017), 1002–1020.
356. Zhang, L., Ji, Q., Image segmentation with a unified graphical model. IEEE Trans. Pattern Anal. Mach. Intell. 32 (2010), 1406–1425, 10.1109/TPAMI.2009.145.
357. Zhang, L., Jia, K., Li, X., Yuan, Q., Zhao, X., Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery. Remote Sens. Lett. 5 (2014), 73–82, 10.1080/2150704X.2013.875235.
358. Zhang, L., Zhang, L., Du, B., Deep learning for remote sensing data. IEEE Geosci. Remote Sens. Mag. 4 (2016), 22–40, 10.1155/2016/7954154.
359. Zhang, X., Feng, X., Xiao, P., Multi-scale segmentation of high-spatial resolution remote sensing images using adaptively increased scale parameter. Photogramm. Eng. Remote Sens. 81 (2015), 461–470 https://doi.org/10.14358/PERS.81.6.461.
360. Zhang, X., Feng, X., Xiao, P., He, G., Zhu, L., Segmentation quality evaluation using region-based precision and recall measures for remote sensing images. ISPRS J. Photogramm. Remote Sens. 102 (2015), 73–84, 10.1016/j.isprsjprs.2015.01.009.
361. Zhang, X., Xiao, P., Feng, X., Toward combining thematic information with hierarchical multiscale segmentations using tree Markov random field model. ISPRS J. Photogramm. Remote Sens. 131 (2017), 134–146, 10.1016/j.isprsjprs.2017.08.003.
362. Zhang, X., Xiao, P., Feng, X., Fast hierarchical segmentation of high-resolution remote sensing image with adaptive edge penalty. Photogramm. Eng. Remote Sens. 80 (2014), 71–80 https://doi.org/10.14358/PERS.80.1.71.
363. Zhang, X., Xiao, P., Feng, X., Feng, L., Ye, N., Toward evaluating multiscale segmentations of high spatial resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 53 (2015), 3694–3706, 10.1109/TGRS.2014.2381632.
364. Zhang, X., Xiao, P., Feng, X., Wang, J., Wang, Z., Hybrid region merging method for segmentation of high-resolution remote sensing images. ISPRS J. Photogramm. Remote Sens. 98 (2014), 19–28, 10.1016/j.isprsjprs.2014.09.011.
365. Zhang, X., Xiao, P., Song, X., She, J., Boundary-constrained multi-scale segmentation method for remote sensing images. ISPRS J. Photogramm. Remote Sens. 78 (2013), 15–25, 10.1016/j.isprsjprs.2013.01.002.
366. Zhang, Y.-J., An Overview of Image and Video Segmentation in the Last 40 Years. Zhang, Y.-.J., (eds.) Advances in Image and Video Segmentation, 2006, IRM Press, Pennsylvania, USA, 1–15.
367. Zhang, Y., Feng, X., Le, X., Segmentation on Multispectral Remote Sensing Image Using Watershed Transformation. Congress on Image and Signal Processing, 2008, 2008, IEEE, 773–777, 10.1109/CISP.2008.365.
368. Zhang, Y., Matuszewski, B.J., Shark, L.-K., Moore, C.J., Medical Image Segmentation Using New Hybrid Level-Set Method. 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics, 2008, IEEE, London, UK, 71–76, 10.1109/MediVis.2008.12.
369. Zhang, Y.J., Evaluation and comparison of different segmentation algorithms. Pattern Recognit. Lett. 18 (1997), 963–974 https://doi.org/https://doi.org/10.1016/S0167-8655(97)00083-4.
370. Zhang, Y.J., A survey on evaluation methods for image segmentation. Pattern Recognit. 29 (1996), 1335–1346, 10.1016/0031-3203(95)00169-7.
371. Zhao, M., Li, F., Tang, G., Optimal Scale Selection for DEM Based Slope Segmentation in the Loess Plateau. Int. J. Geosci. 03 (2012), 37–43, 10.4236/ijg.2012.31005.
372. Zheng, C., Wang, L., Chen, R., Chen, X., Image Segmentation Using Multiregion-Resolution MRF Model. IEEE Geosci. Remote Sens. Lett. 10 (2013), 816–820.
373. Zhengqin, Li, Jiansheng, Chen, 2015. Superpixel segmentation using Linear Spectral Clustering. 2015 IEEE Conf. Comput. Vis. Pattern Recognit. 1356–1363. https://doi.org/10.1109/CVPR.2015.7298741.
374. Zhong, Y., Gao, R., Zhang, L., Multiscale and Multifeature Normalized Cut Segmentation for High Spatial Resolution Remote Sensing Imagery. IEEE Trans. Geosci. Remote Sens. 54 (2016), 6061–6075, 10.1109/TGRS.2016.2580643.
375. Zhou, C., Wang, P., Zhang, Z., Qi, C., Wang, Y., Object-oriented information extraction technology from QuickBird pan-sharpened images. Int. Soc. Opt. Eng., 6279, 2007, 10.1117/12.725360.
376. Zhou, H., Kong, H., Wei, L., Creighton, D., Nahavandi, S., On Detecting Road Regions in a Single UAV Image. IEEE Trans. Intell. Transp. Syst. 18 (2016), 1713–1722, 10.1109/TITS.2016.2622280.
377. Zhou, Y., Feng, L., Chen, Y., Li, J., Object-based land cover mapping using adaptive scale segmentation from ZY-3 satellite images. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017, IEEE, 63–66, 10.1109/IGARSS.2017.8126894.
378. Zhou, Y., Li, J., Feng, L., Zhang, X., Hu, X., Adaptive Scale Selection for Multiscale Segmentation of Satellite Images. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 10 (2017), 3641–3651, 10.1109/JSTARS.2017.2693993.
379. Zhou, Y.T., Venkateswar, V., Chellappa, R., Edge detection and linear feature extraction using a 2-D random field model. IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989), 84–95, 10.1109/34.23115.
380. Zhuowen, Tu, Zhu, Song-Chun, Image segmentation by data-driven markov chain monte carlo. IEEE Trans. Pattern Anal. Mach. Intell. 24 (2002), 657–673, 10.1109/34.1000239.
381. Zivkovic, Z., Improved adaptive Gaussian mixture model for background subtraction. 17th International Conference on Pattern Recognition, 2004. ICPR2004., 2004, 28–31, 10.1109/ICPR.2004.1333992.
382. Zouagui, T., Benoit-Cattin, H., Odet, C., Image segmentation functional model. Pattern Recognit. 37 (2004), 1785–1795, 10.1016/j.patcog.2003.12.014.
383. Zuva, T., Olugbara, O.O., Ojo, S.O., Ngwira, S.M., Image Segmentation, Available Techniques, Developments and Open Issues. Can. J. Image Process. Comput. Vis. 2 (2011), 20–29.