Toward Evaluating Multiscale Segmentations of High Spatial Resolution Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing

Volumn 53, Issue 7, 2015, Pages 3694-3706

  Zhang, Xueliang ^a   Xiao, Pengfeng ^a   Feng, Xuezhi ^a   Feng, Li ^a   Ye, Nan ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

High spatial resolution remote sensing; image segmentation; multiscale segmentation accuracy; object based image analysis (OBIA); scale selection; supervised evaluation

Indexed keywords

IMAGE ANALYSIS; IMAGE RECONSTRUCTION; IMAGE RESOLUTION; REMOTE SENSING;

HIGH SPATIAL RESOLUTION; MULTISCALE SEGMENTATION; OBJECT BASED IMAGE ANALYSIS (OBIA); SCALE SELECTION; SUPERVISED EVALUATION;

IMAGE SEGMENTATION;

ACCURACY ASSESSMENT; ALGORITHM; GEOGRAPHICAL REGION; OPTIMIZATION; PARAMETERIZATION; REMOTE SENSING; SPATIAL RESOLUTION;

CHINA; HANGZHOU; ZHEJIANG;

EID: 85027949952     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2014.2381632     Document Type: Article

References (69)

1. T. Blaschke, "Object based image analysis for remote sensing," ISPRS J. Photogramm. Remote Sens., vol. 65, no. 1, pp. 2-16, Jan. 2010.
2. L. Bruzzone and L. Carlin, "A multilevel context-based system for classification of very high spatial resolution images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 9, pp. 2587-2600, Sep. 2006.
3. T. Blaschke and J. Strobl, "What's wrong with pixels? Some recent developments interfacing remote sensing and GIS," GIS-Zeitschrift für Geoinformationssysteme, vol. 14, no. 6, pp. 12-17, 2001.
4. G. J. Hay and G. Castilla, "Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline," In Object-Based Image Analysis, in Berlin, Germany Springer-Verlag, 2008, pp. 75-89.
5. U. C. Benz, P. Hofmann, G. Willhauck, I. Lingenfelder, and M. Heynen, "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information," ISPRS J. Photogramm. Remote Sens., vol. 58, no. 3/4, pp. 239-258, Jan. 2004.
6. T. Blaschke et al., "Geographic object-based image analysis-Towards a new paradigm," ISPRS J. Photogramm. Remote Sens., vol. 87, pp. 180-191, Jan. 2014.
7. J. Schiewe, L. Tufte, and M. Ehlers, "Potential and problems of multiscale segmentation methods in remote sensing," GIS-Zeitschrift für Geoinformationssysteme, vol. 6, no. 1, pp. 34-39, 2001.
8. Q. Yu et al., "Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery," Photogramm. Eng. Remote Sens., vol. 72, no. 7, pp. 799-811, Jul. 2006.
9. G. Yan, J. F. Mas, B. H. P. Maathuis, Z. Xiangmin, and P. M. Van Dijk, "Comparison of pixel-based and object-oriented image classification approaches - A case study in a coal fire area, Wuda, Inner Mongolia, China," Int. J. Remote Sens., vol. 27, no. 18, pp. 4039-4055, Sep. 2006.
10. S. W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, "Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery," Remote Sens. Environ., vol. 115, no. 5, pp. 1145-1161, May 2011.
11. H. G. Akc¸ay and S. Aksoy, "Automatic detection of geospatial objects using multiple hierarchical segmentations," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2097-2111, Jul. 2008.
12. D. G. Blumberg and G. Zhu, "Using a hierarchical multi-resolution mechanism for the classification and semantic extraction of landuse maps for Beer-Sheva, Israel," Int. J. Remote Sens., vol. 28, no. 5, pp. 3273-3289, Aug. 2007.
13. M. S. Chubey, S. E. Franklin, and M. A. Wulder, "Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters," Photogramm. Eng. Remote Sens., vol. 72, no. 4, pp. 383-394, Apr. 2006.
14. G. J. Hay, G. Castilla, M. A. Wulder, and J. R. Ruiz, "An automated objectbased approach for the multiscale image segmentation of forest scenes,"Int. J. Appl. Earth. Observ., vol. 7, no. 4, pp. 339-359, Dec. 2005.
15. G. Mallinis, N. Koutsias, M. Tsakiri-Strati, and M. Karteris, "Objectbased classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site," ISPRS J. Photogramm. Remote Sens., vol. 63, no. 2, pp. 237-250, Mar. 2008.
16. M. Kim, T. A. Warner, M. Madden, and D. S. Atkinson, "Multiscale GEOBIA with very high spatial resolution digital aerial imagery: Scale, texture and image objects," Int. J. Remote Sens., vol. 32, no. 10, pp. 2825-2850, May 2011.
17. B. Johnson and Z. Xie, "Classifying a high resolution image of an urban area using super-object information," ISPRS J. Photogramm. Remote Sens., vol. 83, pp. 40-49, Sep. 2013.
18. C. Burnett and T. Blaschke, "A multi-scale segmentation/object relationship modeling methodology for landscape analysis," Ecol. Model., vol. 168, no. 3, pp. 233-249, Oct. 2003.
19. G. J. Hay, T. Blaschke, D. J. Marceau, and A. Bouchard, "A comparison of three image-object methods for the multiscale analysis of landscape structure," ISPRS J. Photogramm. Remote Sens., vol. 57, no. 5/6, pp. 327-345, Apr. 2003.
20. E. Borenstein and S. Ullman, "Combined top-down/bottom-up segmentation,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 12, pp. 2109-2125, Dec. 2008.
21. Z. Tu, X. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation, detection, and recognition," Int. J. Comput. Vis., vol. 63, no. 2, pp. 113-140, Feb. 2005.
22. N. S. Anders, A. Seijmonsbergen, and W. Bouten, "Segmentation optimization and stratified object-based analysis for semi-automated geomorphological mapping," Remote Sens. Environ., vol. 115, no. 12, pp. 2976-2985, Dec. 2011.
23. J. Schiewe, "Segmentation of high-resolution remotely sensed data - Concepts, applications and problems," in Int. Archives Photogramm., Remote Sens. Spatial Inf. Sci., 2002, vol. 34, pp. 380-385.
24. I. Dronova, P. Gong, and L. Wang, "Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China," Remote Sens. Environ., vol. 115, no. 12, pp. 3220-3236, Dec. 2011.
25. R. Raši et al., "An automated approach for segmenting and classifying a large sample of multi-date Landsat imagery for pan-tropical forest monitoring," Remote Sens. Environ., vol. 115, no. 12, pp. 3659-3669, Dec. 2011.
26. M. Neubert, H. Herold, and G. Meinel, "Assessing image segmentation quality - Concepts, methods and application," in Object-Based Image Analysis, Berlin, Germany: Springer-Verlag, pp. 769-784, 2008.
27. Y. J. Zhang, "A survey on evaluation methods for image segmentation,"Pattern Recognit., vol. 29, no. 8, pp. 1335-1346, Aug. 1996.
28. G. Meinel and M. Neubert, "A comparison of segmentation programs for high resolution remote sensing data," in Proc. Int. Archives Photogramm., Remote Sens., 2004, vol. 35, pp. 1097-1105.
29. N. Clinton, A. Holt, J. Scarborough, Y. Li, and P. Gong, "Accuracy assessment measures for object-based image segmentation goodness,"Photogramm. Eng. Remote Sens., vol. 76, no. 3, pp. 289-299, Mar. 2010.
30. F. J. Estrada and A. D. Jepson, "Benchmarking image segmentation algorithms,"Int. J. Comput. Vis., vol. 85, no. 2, pp. 167-181, May 2009.
31. Y. Liu et al., "Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis," ISPRS J. Photogramm. Remote Sens., vol. 68, pp. 144-156, Mar. 2012.
32. A. Lucieer, "Uncertainties in segmentation and their visualization," Ph.D. dissertation, Utrecht Univ. Int. Inst. Geo-Inf. Sci. Earth Observ., Enschede, The Netherlands, 2004.
33. P. R. Marpu, M. Neubert, H. Herold, and I. Niemeyer, "Enhanced evaluation of image segmentation results," J. Spatial Sci., vol. 55, no. 1, pp. 55-68, Jun. 2010.
34. M. Möller, L. Lymburner, and M. Volk, "The comparison index: A tool for assessing the accuracy of image segmentation," Int. J. Appl. Earth. Observ., vol. 9, no. 3, pp. 311-321, Aug. 2007.
35. C. Persello and L. Bruzzone, "A novel protocol for accuracy assessment in classification of very high resolution images," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 3, pp. 1232-1244, Mar. 2010.
36. A. Räsänen, A. Rusanen, M. Kuitunen, and A. Lensu, "What makes segmentation good? A case study in boreal forest habitat mapping," Int. J. Remote Sens., vol. 34, no. 23, pp. 8603-8627, Oct. 2013.
37. C. Witharana, D. L. Civco, and T. H. Meyer, "Evaluation of data fusion and image segmentation in earth observation based rapidmappingworkflows,"ISPRS J. Photogramm. Remote Sens., vol. 87, pp. 1-18, Jan. 2014.
38. Q. Zhan, M. Molenaar, K. Tempfli, and W. Shi, "Quality assessment for geo-spatial objects derived from remotely sensed data," Int. J. Remote Sens., vol. 26, no. 14, pp. 2953-2974, Jul. 2005.
39. Y. J. Zhang, "Evaluation and comparison of different segmentation algorithms,"Pattern Recognit. Lett., vol. 18, no. 10, pp. 963-974, Oct. 1997.
40. S. Chabrier, B. Emile, C. Rosenberger, and H. Laurent, "Unsupervised performance evaluation of image segmentation," EURASIP J. Appl. Signal Process., vol. 2006, no. 1, pp. 096306-1-096306-12, Mar. 2006.
41. J. Chen, J. Li, D. Pan, Q. Zhu, and Z. Mao, "Edge-guided multiscale segmentation of satellite multispectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4513-4520, Nov. 2012.
42. P. Corcoran, A. Winstanley, and P. Mooney, "Segmentation performance evaluation for object-based remotely sensed image analysis," Int. J. Remote Sens., vol. 31, no. 3, pp. 617-645, Feb. 2010.
43. L. Drəguţ, D. Tiede, and S. R. Levick, "ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data," Int. J. Geogr. Inf. Sci., vol. 24, no. 6, pp. 859-871, Mar. 2010.
44. G. M. Espindola, G. Câmara, I. A. Reis, L. S. Bins, and A. M. Monteiro, "Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation," Int. J. Remote Sens., vol. 27, no. 14, pp. 3035-3040, Jul. 2006.
45. D. Faur, I. Gavat, and M. Datcu, "Salient remote sensing image segmentation based on rate-distortion measure," IEEE Geosci. Remote Sens. Lett., vol. 6, no. 4, pp. 855-859, Oct. 2009.
46. Y. Gao, J. F. Mas, N. Kerle, and J. A. Navarrete Pacheco, "Optimal region growing segmentation and its effect on classification accuracy," Int. J. Remote Sens., vol. 32, no. 13, pp. 3747-3763, Jul. 2011.
47. B. Johnson and Z. Xie, "Unsupervised image segmentation evaluation and refinement using a multi-scale approach," ISPRS J. Photogramm. Remote Sens., vol. 66, no. 4, pp. 473-483, Jul. 2011.
48. A. Stein and K. De Beurs, "Complexity metrics to quantify semantic accuracy in segmented Landsat images," Int. J. Remote Sens., vol. 26, no. 14, pp. 2937-2951, Jul. 2005.
49. H. Zhang, J. E. Fritts, and S. A. Goldman, "Image segmentation evaluation: A survey of unsupervised methods," Comput. Vis. Image Understand., vol. 110, no. 2, pp. 260-280, May. 2008.
50. X. Zhang, P. Xiao, and X. Feng, "An unsupervised evaluation method for remotely sensed imagery segmentation," IEEE Geosci. Remote Sens. Lett., vol. 9, no. 2, pp. 156-160, Mar. 2012.
51. L. Dronova et al., "Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods," Remote Sens. Environ., vol. 127, pp. 357-369, Dec. 2012.
52. A. S. Laliberte and A. Rango, "Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery,"IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 761-770, Mar. 2009.
53. R. Unnikrishnan, C. Pantofaru, and M. Hebert, "Toward objective evaluation of image segmentation algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 929-944, Jun. 2007.
54. T. Esch, M. Thiel, M. Bock, A. Roth, and S. Dech, "Improvement of image segmentation accuracy based on multiscale optimization procedure," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 463-467, Jul. 2008.
55. L. Drəguţ, O. Csillik, C. Eisank, and D. Tiede, "Automated parameterisation for multi-scale image segmentation on multiple layers,"ISPRS J. Photogramm. Remote Sens., vol. 88, pp. 119-127, Feb. 2014.
56. J. Yang, P. Li, and Y. He, "A multi-band approach to unsupervised scale parameter selection for multi-scale image segmentation," ISPRS J. Photogramm. Remote Sens., vol. 94, pp. 13-24, Apr. 2014.
57. R. Trias-Sanz, G. Stamon, and J. Louchet, "Using colour, texture, and hierarchical segmentation for high-resolution remote sensing," ISPRS J. Photogramm. Remote Sens., vol. 63, no. 2, pp. 156-168, Mar. 2008.
58. D. R. Martin, "An empirical approach to grouping and segmentation,"Ph.D. dissertation, Comput. Sci. Div., Univ. California, Berkeley, Berkeley, CA, USA, Rep. UCB/CSD-3-1268, Aug. 2003.
59. Y. Zhang, "Problems in the fusion of commercial high-resolution satellite images as well as Landsat 7 images and initial solutions," Int. Archives Photogramm., Remote Sens., vol. 34, pt. 4, Jul. 2002.
60. M. Baatz and A. Schäpe, "Multiresolution segmentation - An optimization approach for high quality multi-scale image segmentation," in Angewandte Geographische Informations-Verarbeitung XII, J., Strobl and T., Blaschke and G. Griesebner Eds. Karlsruhe, Germany: Wichmann Verlag, 2000, pp. 12-23.
61. X. Zhang, P. Xiao, and X. Feng, "Fast hierarchical segmentation of high-resolution remote sensing image with adaptive edge penalty,"Photogramm. Eng. Remote Sens., vol. 80, no. 1, pp. 71-80, Jan. 2014.
62. D. Comaniciu and P. Meer, "Mean shift: A robust approach toward feature space analysis," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 5, pp. 603-619, May 2002.
63. J. S. Cardoso and L. Corte-Real, "Toward a generic evaluation of image segmentation," IEEE Trans. Image Process., vol. 14, no. 11, pp. 1773-1782, Nov. 2005.
64. L. Hubert and P. Arabie "Comparing partitions," J. Classification, vol. 2, no. 1, pp. 193-218, Dec. 1985.
65. A. P. Carleer, O. Debeir and E. Wolff, "Assessment of very high spatial resolution satellite image segmentations," Photogramm. Eng. Remote Sens., vol. 71, no. 11, pp. 1285-1294, Nov. 2005.
66. C. Witharana and D. L. Civco, "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., vol. 87, pp. 108-121, Jan. 2014.
67. D. Comaniciu, "An algorithm for data-driven bandwidth selection,"IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 2, pp. 281-288, Feb. 2003.
68. X. Huang and L. Zhang, "An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery,"IEEE Trans. Geosci. Remote Sens., vol. 46, no. 12, pp. 4173-4185, Dec. 2008.
69. th Int. Symp. Spatial Accuracy Assess. Nat. Resour. Environ. Sci., Leicester, U.K., 2010, pp. 13-16.