Polarimetric contextual classification of PolSAR images using sparse representation and superpixels

Remote Sensing

Volumn 6, Issue 8, 2014, Pages 7158-7181

  Feng, Jilan ^a   Cao, Zongjie ^a   Pi, Yiming ^a  

^a UNIVERSITY OF ELECTRONIC SCIENCE AND TECHNOLOGY OF CHINA   (China)

Author keywords

Image classification; Joint sparse representation; Polarimetric synthetic aperture radar (PolSAR); Sparse representation based classifier; Spatial regularization; Superpixel

Indexed keywords

CLASSIFICATION (OF INFORMATION); PATTERN RECOGNITION; PIXELS; POLARIMETERS; RADAR; SYNTHETIC APERTURE RADAR;

CLASSIFICATION PERFORMANCE; CONTEXTUAL CLASSIFICATION; PATTERN RECOGNITION PROBLEMS; POLARIMETRIC INFORMATIONS; POLARIMETRIC SYNTHETIC APERTURE RADARS; SPARSE REPRESENTATION; SPATIAL REGULARIZATIONS; SUPER PIXELS;

IMAGE CLASSIFICATION;

EID: 84997447910     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs6087158     Document Type: Article

References (68)

1. Touzi, R.; Boerner, W.M.; Lee, J.S.; Lueneburg, E. A review of polarimetry in the context of synthetic aperture radar: Concepts and information extraction. Can. J. Remote Sens. 2004, 30, 380-407.
2. Novak, L.M.; Halversen, S.D.; Owirka, G.J.; Hiett, M. Effects of polarization and resolution on SAR ATR. IEEE Trans. Aerosp. Electron. Syst. 1997, 33, 102-116.
3. Lee, J.S.; Grunes, M.R.; Pottier, E. Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR. IEEE Trans. Geosci. Remote Sens. 2001, 39, 2343-2351.
4. Cloude, S.R.; Pottier, E. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans. Geosci. Remote Sens. 1997, 35, 68-78.
5. Ferro-Famil, L.; Pottier, E.; Lee, J.S. Unsupervised classification of multifrequency and fully polarimetric SAR images based on H/a/Alpha-Wishart classifier. IEEE Trans. Geosci. Remote Sens. 2001, 39, 2332-2342.
6. Hoekman, D.H.; Vissers, M.A.; Tran, T.N. Unsupervised full-polarimetric SAR data segmentation as a tool for classification of agricultural areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 4, 402-411.
7. Quegan, S.; Rhodes, I. Statistical models for polarimetric data: Consequences, testing and validity. Int. J. Remote Sens. 1995, 16, 1183-1210.
8. Freitas, C.C.; Frery, A.C.; Correia, A.H. The polarimetric G distribution for SAR data analysis. Environmetrics 2005, 16, 13-31.
9. Cloude, S.R.; Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens. 1996, 34, 498-518.
10. Lee, J.S.; Grunes, M.R.; Kwok, R. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution. Int. J. Remote Sens. 1994, 15, 2299-2311.
11. Doulgeris, A.; Anfinsen, S.; Eltoft, T. Automated non-Gaussian clustering of polarimetric synthetic aperture radar images. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3665-3676.
12. Formont, P.; Pascal, F.; Vasile, G. Statistical classification for heterogeneous polarimetric SAR images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2011, 5, 567-576.
13. Conradsen, K.; Nielsen, A.; Schou, J.; Skriver, H. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Trans. Geosci. Remote Sens. 2003, 41, 4-19.
14. Wu, Y.; Ji, K.; Yu, W.; Su, Y. Segmentation of multi-look fully polarimetric SAR images based on Wishart distribution and MRF. Proc. SPIE 2007, 6748, 674813:1-674813:9.
15. Wu, Y.; Ji, K.; Yu, W.; Su, Y. Region-based classification of polarimetric SAR images using Wishart MRF. IEEE Geosci. Remote Sens. Lett. 2008, 5, 668-672.
16. Ayed, I.B.; Mitiche, A.; Belhadj, Z. Polarimetric image segmentation via maximum-likelihood approximation and efficient multiphase level-sets. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28, 1493-1500.
17. Gao, W.; Yang, J.; Ma, W. Land cover classification for polarimetric SAR images based on mixture models. Remote Sens. 2014, 6, 3770-3790.
18. Anfinsen, S.; Eltoft, T. Application of the matrix-variate Mellin transform to analysis of polarimetric radar images. IEEE Trans. Geosci. Remote Sens. 2011, 49, 2281-2781.
19. Anfinsen, S.; Doulgeris, A.; Eltoft, T. Goodness-of-fit tests for multilook polarimetric radar data based on the Mellin transform. IEEE Trans. Geosci. Remote Sens. 2011, 49, 2764-2295.
20. Van Zyl, J.J. Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Trans. Geosci. Remote Sens. 1989, 27, 36-45.
21. Lee, J.S.; Grunes, M.R.; Pottier, E.; Ferro-Famil, L. Unsupervised terrain classification preserving polarimetric scattering characteristics. IEEE Trans. Geosci. Remote Sens. 2004, 42, 722-731.
22. Yonezawa, C.; Watanabe, M.; Saito, G. Polarimetric decomposition analysis of ALOS PALSAR observation data before and after a landslide event. Remote Sens. 2012, 4, 2314-2328.
23. Huynen, J.R. Phenomenological Theory of Radar Targets. Ph.D. Dissertation, Delft University, Delft, The Netherlands, 1970.
24. Yamaguchi, Y.; Moriyama, T.; Ishido, M.; Yamada, H. Four component scattering model for polarimetric SAR image decomposition. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1699-1706.
25. Cameron, W.L.; Youssef, N.N.; Leung, L.K. Simulated polarimetric signatures of primitive geometrical shapes. IEEE Trans. Geosci. Remote Sens. 1996, 34, 793-803.
26. Krogager, E. New decomposition of the radar target scattering matrix. Electron. Lett. 1990, 26, 1525-1527.
27. Lardeux, C.; Frison, P.L.; Tison, C.; Stoll, B.; Fruneau, B.; Rudant, J.-P. Support vector machine for multifrequency SAR polarimetric data classification. IEEE Trans. Geosci. Remote Sens. 2009, 47, 4143-4152.
28. Zou, T.Y.; Yang, W.; Dai, D.X.; Sun, H. Polarimetric SAR image classification using multifeatures combination and extremely randomized clustering forests. EURASIP J. Adv. Signal Process. 2010, 2010, 465612:1-465612:9.
29. Tu, S.T.; Chen, J.Y.; Yang, W.; Sun, H. Laplacian eigenmaps based polarimetric dimensionality reduction for SAR image classification. IEEE Trans. Geosci. Remote Sens. 2012, 50, 170-179.
30. Shi, L.; Zhang, L.; Yang, J. Supervised graph embedding for polarimetric SAR image classification. IEEE Geosci. Remote Sens. Lett. 2013, 10, 216-220.
31. Pajares, G.; López-Martínez, C.; Sánchez-Lladó, F.J.; Molina, í. Improving Wishart classification of polarimetric SAR data using the hopfield neural network optimization approach. Remote Sens. 2012, 4, 3571-3595.
32. She, X.L.; Yang, J.; Zhang, W.J. The boosting algorithm with application to polarimetric SAR image classification. In Proceedings of the 1st Asian and Pacific Conference on Synthetic Aperture Radar, Huangshan, China, 5-9 November 2007; pp. 779-783.
33. Wright, J.; Yang, A.Y.; Ganesh, A.; Sastry, S.S.; Ma, Y. Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 31, 210-227.
34. Wright, J.; Ma, Y.; Mairal, J.; Sapiro, G.; Huang, T.S.; Yan, S.C. Sparse representation for computer vision and pattern recognition. Proc. IEEE 2010, 98, 1031-1044.
35. Yang, J.; Wright, J.; Huang, T.; Ma, Y. Image super-resolution as sparse representation of raw image patches. In Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 23-28 June 2008; pp. 1-8.
36. Elad, M.; Aharon, M. Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 2006, 15, 3736-3745.
37. Guo, Z.; Wittman, T.; Osher, S. L1 unmixing and its application to hyperspectral image enhancement. Proc SPIE 2009, 7334, doi:10.1117/12.818245.
38. Schindler, K. An overview and comparison of smooth labeling methods for land-cover classification. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4534-4545.
39. Fauvel, M.; Tarabalka, Y.; Benediktsson, J.A. Advances in spectral-spatial classification of hyperspectral images. Proc. IEEE 2013, 101, 652-675.
40. Moser, G.; Serpico, S.; Benediktsson, J.A. Land-cover mapping by Markov modeling of spatial-contextual information in very-high-resolution remote sensing images. Proc. IEEE 2013, 101, 631-651.
41. Benediktsson, J.A.; Palmason, J.A.; Sveinsson, J.R. Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. Geosci. Remote Sens. 2005, 43, 480-491.
42. Tuia, D.; Pacifici, F.; Kanevski, M.; Emery, W.J. Classification of very high spatial resolution imagery using mathematical morphology and support vector machines. IEEE Trans. Geosci. Remote Sens. 2009, 47, 3866-3879.
43. Mura, M.D.; Villa, A.; Benediktsson, J.A.; Chanussot, J.; Bruzzone, L. Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis. IEEE Geosci. Remote Sens. Lett. 2011, 8, 542-546.
44. Chen, Y.; Nasrabadi, N.; Tran, T. Hyperspectral image classification using dictionary-based sparse representation. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3973-3985.
45. Zhang, H.; Li, J.; Huang, Y.; Zhang, L. A nonlocal weighted joint sparse representation classification method for hyperspectral imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2013, PP, 1-10.
46. Ren, X.; Malik, J. Learning a classification model for segmentation. In Proceedings of the 9th IEEE International Conference on Computer Vision, Los Alamitos, CA, USA, 13-16 October 2003; pp. 10-17.
47. Fulkerson, B.; Vedaldi, A.; Soatto, S. Class segmentation and object localization with superpixel neighborhoods. In Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan, 27 September-4 October 2009; pp. 670-677.
48. Gould, S.; Rodgers, J.; Cohen, D.; Elidan, G.; Koller, D. Multi-class segmentation with relative location prior. Int. J. Comput. Vis. 2008, 80, 300-316.
49. Kohli, P.; Ladicky, L.; Torr, P. Robust higher order potentials for enforcing label consistency. Int. J. Comput. Vis. 2009, 82, 302-324.
50. Chen, X.; Fang, T.; Huo, H.; Li, D. Graph-based feature selection for object-oriented classification in VHR airborne imagery. IEEE Trans. Geosci. Remote Sens. 2011, 49, 353-365.
51. Hou, B.; Zhang, X.; Li, N. MPM SAR image segmentation using feature extraction and context model. IEEE Geosci. Remote Sens. Lett. 2012, 9, 1041-1045.
52. Ersahin, K.; Cumming, I.G.; Ward, R.K. Segmentation and classification of polarimetric SAR data using spectral graph partitioning. IEEE Trans. Geosci. Remote Sens. 2010, 48, 164-174.
53. Liu, B.; Hu, H.; Wang, H.; Wang, K.; Liu, X.; Yu, X. Superpixel-based classification with an adaptive number of classes for polarimetric SAR images. IEEE Trans. Geosci. Remote Sens. 2013, 51, 907-924.
54. Achanta, R.; Shaji, A.; Smithet, K.; Lucchi, A.; Fua, P.; Susstrunk, S. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 2012, 34, 2274-2282.
55. Molinier, M.; Laaksonen, J.; Rauste, Y.; Häme, T. Detecting changes in polarimetric SAR data with content-based image retrieval. In Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23-28 June 2007; pp. 2390-2393.
56. Tropp, J.; Gilbert, A. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inf. Theory 2007, 53, 4655-4666.
57. Donoho, D. For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 2006, 59, 797-829.
58. Candes, E.; Tao, T. Near-optimal signal recovery from random projections: Universal encoding strategies? IEEE Trans. Inf. Theory 2006, 52, 5406-5425.
59. Chen, S.; Donoho, D.; Saunders, M. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 1998, 20, 1641-1643.
60. Yang, A.Y.; Zhou, Z. Fast l1-minimization algorithms for robust face recognition. IEEE Trans. Image Process. 2013, 22, 3234-3246.
61. Vincent, L.; Soille, P. Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Mach. Intell. 1991, 13, 583-598.
62. Shi, J.; Malik, J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22, 888-905.
63. Tarabalka, Y.; Benediktsson, J.A.; Chanussot, J. Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques. IEEE Trans. Geosci. Remote Sens. 2009, 47, 2973-2987.
64. Wakin, M.; Duarte, M.; Sarvotham, S.; Baron, D.; Baraniuk, R. Recovery of jointly sparse signals from few random projections. In Proceedings of the Advances in Neural Information Processing Systems 18, 2005 Annual Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 5-8 December 2005; pp. 1435-1442.
65. Cotter, S.F.; Rao, B.D.; Engan, K.; Kreutz-Delgado, K. Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Trans. Signal Process. 2005, 53, 2477-2488.
66. Rakotomamonjy, A. Surveying and comparing simultaneous sparse approximation (or group lasso) algorithms. Signal Process. 2011, 91, 1505-1526.
67. Boykov, Y.; Kolmogorov, V. An experimental comparison of mincut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 26, 1124-1137.
68. Camps-Valls, G.; Gomez-Chova, L.; Munoz-Mari, J.; Vila-Frances, J.; Calpe-Maravilla, J. Composite kernels for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 2006, 3, 93-97.