Local and global evaluation for remote sensing image segmentation

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 130, Issue , 2017, Pages 256-276

  Su, Tengfei ^a   Zhang, Shengwei ^a  

^a INNER MONGOLIA AGRICULTURAL UNIVERSITY   (China)

Author keywords

Image segmentation; Local evaluation; Object based image analysis; Region over lapping; Remote sensing

Indexed keywords

ERRORS; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; REMOTE SENSING;

EVALUATION STRATEGIES; HIGH RESOLUTION IMAGE; LOCAL ERROR DISTRIBUTION; LOCAL EVALUATION; OBJECT BASED IMAGE ANALYSIS; REMOTE SENSING IMAGES; SEGMENTATION EVALUATION; SEGMENTATION RESULTS;

IMAGE SEGMENTATION;

ACCURACY ASSESSMENT; ERROR ANALYSIS; GLOBAL PERSPECTIVE; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE PROCESSING; IMAGE RESOLUTION; PERFORMANCE ASSESSMENT; REMOTE SENSING; SEGMENTATION; SPATIAL ANALYSIS; VALUATION;

EID: 85021105660     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2017.06.003     Document Type: Article

References (41)

1. Baatz, M., Schäpe, M., Multiresolution segmentation - an optimization approach for high quality multi-scale image segmentation. Angewandte Geographische InformationsVerarbeitung XII. 12–23.Blaschke, T., 2010. Object-based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2000), 2–16.
2. Beaulieu, J., Goldberg, M., Hierarchy in picture segmentation: a step-wise optimization approach. IEEE Trans. Pattern Anal. Mach. Intel. 11 (1989), 150–163.
3. Blaschke, T., Object-based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65 (2010), 2–16.
4. Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., vander 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.
5. 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.
6. 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.
7. Fu, G., Zhao, H., Li, C., Shi, L., Segmentation for high-resolution optical remote sensing imagery using improved quadtree and region adjacency graph technique. Remote Sens. 2013:5 (2013), 3259–3279.
8. 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.
9. Hoover, A., An experimental comparison of range image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 18 (1996), 673–689.
10. Hubert, L., Arabie, P., Comparing partitions. J. Classif. 2 (1985), 193–218.
11. Johnson, B., Xie, Z., Unsupervised image segmentation evaluation and refinement using a multi-scale approach. ISPRS J. Photogramm. Remote Sens. 66 (2011), 473–483.
12. Kim, H., Yeom, J., Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data. Int. J. Remote Sens. 35 (2014), 7046–7068.
13. Liu, D., Xia, F., Assessing object-based classification: advantages and limitations. Remote Sens. Lett. 1 (2010), 187–194.
14. 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.
15. Löw, F., Knöfel, P., Conrad, C., Analysis of uncertainty in multi-temporal object-based classification. ISPRS J. Photogramm. Remote Sens. 105 (2015), 91–106.
16. Marpu, P.R., Neubert, M., Herold, H., Niemeyer, I., Enhanced evaluation of image segmentation results. J. Spat. Sci. 55 (2010), 55–68.
17. Martin, D.R., 2003. An empirical approach to grouping and segmentation. Doctoral Dissertation, Computer Science Division, University of California, Berkeley.
18. McNabb, R., Womble, J., Prakash, A., Gens, R., Haselwimmer, C., Quantification and analysis of icebergs in a tidewater glacier fjord using an object-based approach. PLoS ONE 11 (2016), 1–16.
19. Mikes, S., Haindl, M., Scarpa, G., Remote sensing segmentation benchmark. Proc. IAPR Workshop PRRS, 2012, 1–4.
20. 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.
21. Novelli, A., Aguilar, M.A., Aguilar, F.J., Nemmaoui, A., Tarantino, E., AssesSeg – a command line tool to quantify image segmentation quality: a test carried out in southern Spain from satellite imagery. Remote Sens. 9 (2017), 1–11.
22. Ortiz, A., Oliver, G., On the use of the overlapping area matrix for image segmentation evaluation: a survey and new performance measures. Pattern Recogn. Lett. 27 (2006), 1916–1926.
23. Peña, J., Gutiérrez, P., Hervás-Martínez, C., Six, J., Plant, R., López-Granados, F., Object-based image classification of summer crops with machine learning methods. Remote Sens. 6 (2014), 5019–5041.
24. Schultz, B., Immitzer, M., Formaggio, A.R., Sanches, I.D.A., Luiz, A.J.B., Atzberger, C., Self-guided segmentation and classification of multi-temporal Landsat 8 images for crop type mapping in southeastern Brazil. Remote Sens. 7 (2015), 14482–14508.
25. Son, N., Chen, C., Chang, N., Chen, C., Chang, L., Thanh, B., Mangrove mapping and change detection in Ca Mau peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE J. Sel. Top. App. Earth Obs. Remote Sens. 8 (2015), 3694–3706.
26. Su, T., Li, H., Zhang, S., Li, Y., Image segmentation using mean shift for extracting croplands from high-resolution remote sensing imagery. Remote Sens. Lett. 6 (2015), 952–961.
27. 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.
28. Vidal-Fernández, E., Piedra-Fernández, J., Almendros-Jiménez, J., Cantón-Garbín, M., OBIA system for identifying mesoscale oceanic structures in seawifs and modis-aqua images. IEEE J. Sel. Top. App. Earth Obs. Remote Sens. 8 (2015), 628–637.
29. Vieira, M.A., Formaggio, A.R., Rennó, C.D., Atzberger, C., Aguiar, D.A., Mello, M.P., Remote Sens. Environ. 123 (2012), 1256–1265.
30. Voltersen, M., Berger, C., Hese, S., Schmullius, C., Object-based land cover mapping and comprehensive feature calculation for an automated derivation of urban structure types at block level. Remote Sens. Environ. 154 (2014), 192–201.
31. Wang, M., Huang, J., Ming, D., Region-line association constraints for high-resolution image segmentation. IEEE J. Sel. Top. App. Earth Obs. Remote Sens. 10 (2017), 628–637.
32. 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.
33. Wolpert, D.H., Macready, W.G., No free lunch theorems for optimization. IEEE Trans. Evolut. Comput. 1 (1997), 67–82.
34. Xiao, P., Zhang, X., Wang, D., Yuan, M., Feng, X., Kelly, M., Change detection of built-up land: a framework of combining pixel-based detection and object-based recognition. ISPRS J. Photogramm. Remote Sens. 119 (2016), 402–414.
35. 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.
36. 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.
37. 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.
38. Yi, L., Zhang, G., Wu, Z., A scale-synthesis method for high spatial resolution remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 50 (2012), 4062–4070.
39. Yu, W., Zhou, W., Qian, Y., Yan, J., A new approach for land cover classification and change analysis: integrating backdating and an object-based method. Remote Sens. Environ. 177 (2016), 37–47.
40. 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.
41. 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.