Multiscale Segmentation and Classification of Remote Sensing Imagery with Advanced Edge and Scale-Space Features

Scale Issues in Remote Sensing

Volumn 9781118305041, Issue , 2014, Pages 170-196

  Tzotsos, Angelos ^a   Karantzalos, Konstantinos ^a   Argialas, Demetre ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Edge based segmentation; Kernel based classification; Multiscale object based image analysis; Multiscale segmentation; Radar satellite imagery; Remote sensing imagery; Scale space image representations

Indexed keywords

ANTENNAS; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; RADAR IMAGING; REMOTE SENSING; SATELLITE IMAGERY; SPACE OPTICS; SPACE-BASED RADAR;

EDGE-BASED; EDGE-BASED SEGMENTATION; IMAGE REPRESENTATIONS; IMAGE-ANALYSIS; KERNEL BASED CLASSIFICATION; MULTISCALE OBJECT-BASED IMAGE ANALYSE; MULTISCALE SEGMENTATION; OBJECT BASED; OBJECTS-BASED; RADAR SATELLITE IMAGERY; RADAR SATELLITES; REMOTE SENSING IMAGERY; SCALE-SPACE IMAGE; SCALE-SPACE IMAGE REPRESENTATION;

IMAGE ANALYSIS;

EID: 84926402659     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781118801628.ch09     Document Type: Chapter

References (78)

1. Akcay, H., and Aksoy, S. 2008. Automatic detection of geospatial objects using multiple hierarchical segmentations. IEEE Transactions on Geoscience and Remote Sensing 46: 2097-2111.
2. Aplin, P., and Smith, G. 2008. Advances in object-based image classification. International Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 37(Part B7): 725-728.
3. Baatz, M., and Schape, A. 2000. Multiresolution segmentation an optimization approach for high quality multi-scale image segmentation. In J. Strobl, et al. (Eds.), Angewandte Geographische Infor-mationsverarbeitung XII. Heidelberg: Wichmann, pp. 12-23.
4. Benz, U., Hofmann, P., Willhauck, G., Lingenfelder, I., and Heynen, M. 2004. Multiresolution, object-oriented fuzzy analysis of remote sensing data for gis ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58(3-4):239-258.
5. Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1): 2-16.
6. Blaschke, T., Burnett, C., and Pekkarinen, A. 2004. Image segmentation methods for objectbased analysis and classification. In S. M.de Jong and F. D.van der Meer (Eds.), Remote Sensing and Digital Image Analysis: Including the Spatial Domain. Dordrecht: Kluwer Academic, pp. 211-236.
7. Blaschke, T., and Hay, G. 2001. Object-oriented image analysis and scale-space: Theory and methods for modeling and evaluating multi-scale landscape structure. Interanational Archive of Photogrammetry and Remote Sensing 34(Part4/W5): 22-29.
8. Blaschke, T., Lang, S., and Hay, G. 2008. Object Based Image Analysis-Spatial Concepts for Knowledge Driven Remote Sensing Applications. New York: Springer.
9. Camps-Valls, G., and Bruzzone, L. 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43(6): 1351-1362.
10. Canny, J. 1986. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 6: 679-698.
11. Carleer, A., Debeir, O., and Wolff, E. 2005. Assessment of very high spatial resolution satellite image segmentations. Photogrammetric Engineering and Remote Sensing 71(11): 1285-1294.
12. Chia, A.Y.S., Rajan, D., Leung, M. K., and Rahardja, S. 2012. Object recognition by discriminative combinations of line segments, ellipses, and appearance features. IEEE Transactions on Pattern Analysis and Machine Intelligence 34: 1758-1772.
13. Christophe, E., and Inglada, J. 2009. Open source remote sensing: Increasing the usability of cutting-edge algorithms. IEEE Geoscience and Remote Sensing Newsletter, 2009, 9-15.
14. Comaniciu, D., and Meer, P. 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24: 603-619.
15. Cufi, X., Munoz, X., Freixenet, J., and Marti, J. 2003. A review of image segmentation techniques integrating region and boundary information. Advances in Imaging and Electron Physics 120: 1-39.
16. Derrode, S., and Mercier, G. 2007. Unsupervised multiscale oil slick segmentation from sar images using a vector hmc model. Pattern Recognition 40: 1135-1147.
17. Doxani, G., Karantzalos, K., and Tsakiri-Strati, M. 2012. Monitoring urban changes based on scale-space filtering and object-oriented classification. International Journal of Applied Earth Observation and Geoinformation 15: 38-48.
18. Dragut, L., Schauppenlehner, T., Muhar, A., Strobl, J., and Blaschke, T. 2009. Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling. Computers & Geosciences 35(9): 1875-1883.
19. Dragut, L., Tiede, D., and Levick, S. R. 2010. ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science 24: 859-871.
20. Duarte-Carvajalino, J., Castillo, P., and Velez Reyes, M. 2007. Comparative study of semiimplicit schemes for nonlinear diffusion in hyperspectral imagery. IEEE Transactions on Image Processing 16(5): 1303-1314.
21. Duarte-Carvajalino, J., Sapiro, G., Velez Reyes, M., and Castillo, P. 2008. Multiscale representation and segmentation of hyperspectral imagery using geometric partial differential equations and algebraic multigrid. IEEE Transactions on Geoscience and Remote Sensing 46(8): 2418-2434.
22. Gilboa, G. 2008. Nonlinear scale space with spatially varying stopping time. IEEE Transactions on Pattern Analysis and Machine Intelligence 30: 2175-2187.
23. Hall, O., and Hay, G. J. 2003. A multiscale object-specific approach to digital change detection. International Journal of Applied Earth Observation and Geoinformation 4(4): 311-327.
24. Hay, G., Blaschke, T., Marceau, D., and Bouchard, A. 2003. A comparison of three imageobject methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing 57(5-6):327-345.
25. Hay, G., and Castilla, G. 2006. Object-based image analysis: Strengths, weaknesses, opportunities and threats. Interanational Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 36(4/C42): on CD-ROM.
26. Hay, G., Castilla, G., Wulder, M., and Ruiz, J. 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation 7(4): 339-359.
27. Hay, G. J., Dub, P., Bouchard, A., and Marceau, D.J. 2002. A scale-space primer for exploring and quantifying complex landscapes. Ecological Modelling 153(1-2):27-49.
28. Hofmann, P., Strobl, J., and Blaschke, T. 2008. A method for adapting global image segmentation methods to images of different resolutions. In International Conference on Geographic Object-Based Image Analysis. ISPRS Volume 38; on CD-ROM.
29. Hsu, C. W., and Lin, C. J. 2002. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2): 415-425.
30. Inglada, J., and Christophe, E. 2009. The orfeo toolbox remote sensing image processing software. In Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009, pp. IV-733.
31. Jiang, H., Yu, S., and Martin, D. 2011. Linear scale and rotation invariant matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 33: 1339-1355.
32. Jimenez, L. O., Rivera-Medina, J. L., Rodriguez-Diaz, E., Arzuaga-Cruz, E., and Ramirez-Velez, M. 2005. Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing 43(4): 844-851.
33. Jung, C. 2007. Unsupervised multiscale segmentation of color images. Pattern Recognition Letters 28: 523-533.
34. Karantzalos, K., and Argialas, D. 2006. Improving edge detection and watershed segmentation with anisotropic diffusion and morphological levelings. International Journal of Remote Sensing 27(24): 5427-5434.
35. Karantzalos, K., Argialas, D., and Paragios, N. 2007. Comparing morphological levelings constrained by different markers. In G. Banon, et al. (Eds.), Mathematical Morphology and Its Applications to Signal and Image Processing, MCT/INPE, Brazil, pp. 113-124.
36. Kermad, C., and Chehdi, K. 2002. Automatic image segmentation system through iterative edge-region co-operation. Image and Vision Computing 20: 541-555.
37. Kimmel, R., Zhang, C., Bronstein, A., and Bronstein, M. 2011. Are mser features really interesting? IEEE Transactions on Pattern Analysis and Machine Intelligence 33: 2316-2320.
38. Klonus, S., Tomowski, D., Ehlers, M., Reinartz, P., and Michel, U. 2012. Combined edge segment texture analysis for the detection of damaged buildings in crisis areas. IEEEJournal of Selected Topics in Applied Earth Observations and Remote Sensing 5: 1118-1128.
39. Koenderink, J. 1984. The structure of images. Biological Cybernetics 50(5): 363-370.
40. Lehmann, G. 2008. Label object representation and manipulation with itk. Insight Journal, 9:1-34.
41. Lennon, M., Mercier, G., and Hubert-Moy, L. 2002. Classification of hyperspectral images with nonlinear filtering and support vector machines. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS'02, Dec 2002, Toronto, Canada, Vol 3, pp. 1670-1672.
42. Lindeberg, T. 1994. Scale-Space Theory in Computer Vision. Dordrecht: Kluwer Academic.
43. Liu, Y., Guo, Q., and Kelly, M. 2008. A framework of region-based spatial relations for nonoverlapping features and its application in object based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing 63(4): 461-475.
44. Martha, T., Kerle, N., van Westen, C., Jetten, V., and Kumar, K. 2011. Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Transactions on Geoscience and Remote Sensing 49: 4928-4943.
45. Matheron, G. 1997. Les Nivellements. Technical Report. Centre de Morphologie Mathematique, Paris, France.
46. Meyer, F. 2004. Levelings, image simplification filters for segmentation. International Journal of Mathematical Imaging and Vision 20: 59-72.
47. Meyer, F., and Maragos, P. 2000. Nonlinear scale-space representation with morphological levelings. Journal of Visual Communication and Image Representation 11: 245-265.
48. Meyer, Y. 1998. The levelings. In Mathematical Morphology and Its Applications to Image and Signal Processing. Dordrecht: Kluwer Academic.
49. Mladinich, C., 2010. An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the american west. GIScience and Remote Sensing 47: 53-77.
50. Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., and Weng, Q. 2011. Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment 115: 1145-1161.
51. Neubert, M., Herold, H., and Meinel, G. 2006. Evaluation of remote sensing image segmentation quality-further results and concepts. Interanational Archive of Photogrammetry, Remote Sensing and Spatial Information Sciences 36(4/C42).
52. Nilufar, S., Ray, N., and Zhang, H. 2012. Object detection with dog scale-space: A multiple kernel learning approach. IEEE Transactions on Image Processing 21: 3744-3756.
53. Ouma,Y., Josaphat, S., and Tateishi,R. 2008.Multiscale remote sensingdata segmentationandpostsegmentation change detection based on logical modeling: Theoretical exposition and experimental results for forestland cover change analysis. Computers & Geosciences 34(7): 715-737.
54. Ouzounis, G., Pesaresi, M., and Soille, P. 2012. Differential area profiles: Decomposition properties and efficient computation. IEEE Transactions on Pattern Analysis and Machine Intelligence 34: 1533-1548.
55. Papari, G., and Petkov, N. 2011. Edge and line oriented contour detection: State of the art. Image and Vision Computing 29: 79-103.
56. Paragios, N., Chen, Y., and Faugeras, O. 2005. Handbook of Mathematical Models of Computer Vision. New York, USA, Springer.
57. Pavlidis, T., and Liow, Y. 1990. Integrating region growing and edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 12: 225-233.
58. Perona, P., and Malik, J. 1990. Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(7): 629-639.
59. Plaza, A., Benediktsson, J. A., Boardman, J., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J., and Trianni, G. 2009. Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment 113(S1):S110-S122.
60. Serra, J. 2000. Connections for sets and functions. Fundamentae Informatica 41: 147-186.
61. Stewart, S., Hay, G., Rosin, P., and Wynn, T. J. 2004. Multiscale structure in sedimentary basins. Journal of Basin Research 16(2): 183-197.
62. Theodoridis, S., and Koutroumbas, K. 2003. Pattern Recognition. New York: Elsevier Academic Press.
63. Tzotsos, A. 2006. A support vector machine approach for object based image analysis. In Proceedings of 1st International Conference on Object-based Image Analysis, OBIA 2006, Salzburg, Austria, July 4-5.
64. Tzotsos, A., and Argialas, D. 2006. Mseg: A generic region-based multi-scale image segmentation algorithm for remote sensing imagery. In Proceedings of ASPRS 2006 Annual Conference, Reno, Nevada, May 1-5, ASPRS (on CD-ROM).
65. Tzotsos, A., and Argialas, D. 2008. Support vector machine classification for object-based image analysis. In T. Blaschke, S. Lang, and G. Hay (Eds.), Object Based Image Analysis-Spatial Concepts for Knowledge Driven Remote Sensing Applications. New York: Springer, pp. 663-679.
66. Tzotsos, A., Iosifidis, C., and Argialas, D. 2008. A hybrid texture-based and region-based multi-scale image segmentation algorithm. In T. Blaschke, S. Lang, and G. Hay (Eds.), Object Based Image Analysis-Spatial Concepts for Knowledge Driven Remote Sensing Applications. New York: Springer, pp. 221-237.
67. Tzotsos, A., Karantzalos, K., and Argialas, D. 2011. Object-based image analysis through nonlinear scale-space filtering. ISPRS Journal of Photogrammetry and Remote Sensing 66: 2-16.
68. Vapnik, V. 1998. Statistical Learning Theory. New York: Wiley.
69. Von Gioi, R., Jakubowicz, J., Morel, J., and Randall, G. 2010. LSD: A fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32:722-732.
70. Von Gioi, R., Jakubowicz, J., Morel, J., and Randall, G. 2012. LSD: A Line Segment Detector. Image Processing On Line.
71. Wang, H., and Oliensis, J. 2010. Generalizing edge detection to contour detection for image segmentation. Computer Vision and Image Understanding 114: 731-744.
72. Weickert, J. 1998. Anisotropic Diffusion in Image Processing. ECMI Series. Stuttgart: Teubner.
73. Weickert, J., Ishikawa, S., and Imiya, A. 1999. Linear scale-space has first been proposed in Japan. Journal of Mathematical Imaging and Vision 10(3): 237-252.
74. Witkin, A. 1983. Scale-space filtering. In International Joint Conference on Artificial Intelligence, Karlsruhe, 1983, pp. 1019-1021.
75. Xu, Y., Huang, S., Ji, H., and Fermller, C. 2012. Scale-space texture description on sift-like textons. Computer Vision and Image Understanding 116: 999-1013.
76. Yu, P., Qin, A., and Clausi, D. 2012. Unsupervised polarimetric sar image segmentation and classification using region growing with edge penalty. IEEE Transactions on Geoscience and Remote Sensing 50: 1302-1317.
77. Zhong, B., Ma, K. K., and Liao, W. 2009. Scale-space behavior of planar-curve corners. IEEE Transactions on Pattern Analysis and Machine Intelligence 31: 1517-1524.
78. Zhou, W., Huang, G., Troy, A., and Cadenasso, M. 2009. Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study. Remote Sensing of Environment 113(8): 1769-1777.