Spectral-spatial classification of hyperspectral images using joint bilateral filter and graph cut based model

Remote Sensing

Volumn 8, Issue 9, 2016, Pages

  Wang, Yi ^a   Song, Haiwei ^a   Zhang, Yan ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

Classification; Graph cut; Hyperspectral images; Joint bilateral filter; Spectral spatial

Indexed keywords

BANDPASS FILTERS; CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS; HYPERSPECTRAL IMAGING; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; INDEPENDENT COMPONENT ANALYSIS; NONLINEAR FILTERING; PIXELS; PROBABILITY; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

BILATERAL FILTERS; DIFFERENT RESOLUTIONS; ENERGY MINIMIZATION PROBLEM; GRAPH CUT; GRAPH-CUT SEGMENTATIONS; QUALITATIVE EVALUATIONS; SPECTRAL-SPATIAL; SPECTRAL-SPATIAL CLASSIFICATION;

IMAGE CLASSIFICATION;

EID: 85019694158     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs8090748     Document Type: Article

References (51)

1. Plaza, A.; Benediktsson, J.A.; Boardman, J.W.; Brazile, J.; Bruzzone, L.; Camps-Valls, G.; Chanussot, J.; Fauvel, M.; Gamba, P.; Gualtieri, A.; et al. Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 2009, 113, S110-S122.
2. Camps-Valls, G.; Tuia, D.; Bruzzone, L.; Benediktsson, J.A. Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Peocess. Mag. 2014, 31, 45-54.
3. Melgani, F.; Bruzzone, L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42, 1778-1790.
4. Camps-Valls, G.; Bruzzone, L. Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2005, 43, 1351-1362.
5. Zhang, Y. Optimisation of building detection in satellite images by combining multispectral classification and texture filtering. ISPRS J. Photogramm. Remote Sens. 1999, 54, 50-60.
6. Huang, X.; Zhang, L. A comparative study of spatial approaches for urban mapping using hyperspectral rosis images over pavia city, northern italy. Int. J. Remote Sens. 2009, 30, 3205-3221.
7. 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.
8. Zhang, L.; Huang, X.; Huang, B.; Li, P. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2950-2961.
9. 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.
10. Shen, L.; Zhu, Z.; Jia, S.; Zhu, J.; Sun, Y. Discriminative gabor feature selection for hyperspectral image classification. IEEE Geosci. Remote Sens. Lett. 2013, 10, 29-33.
11. Chen, C.; Li,W.; Su, H.; Liu, K. Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sens. 2014, 6, 5795-5814.
12. Qian, Y.; Ye, M.; Zhou, J. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Trans. Geosci. Remote Sens. 2013, 51, 2276-2291.
13. Quesada-Barriuso, P.; Argüello, F.; Heras, D.B. Spectral-spatial classification of hyperspectral images using wavelets and extended morphological profiles. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2014, 7, 1177-1185.
14. Quesada-Barriuso, P.; Argüello, F.; Heras, D.B.; Benediktsson, J.A. Wavelet-based classification of hyperspectral images using extended morphological profiles on graphics processing units. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2962-2970.
15. Tarabalka, Y.; Chanussot, J.; Benediktsson, J.A. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognit. 2010, 43, 2367-2379.
16. Huang, X.; Zhang, L. An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 2008, 46, 4173-4185.
17. Ghamisi, P.; Couceiro, M.S.; Fauvel, M.; Benediktsson, J.A. Integration of segmentation techniques for classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 2014, 11, 342-346.
18. Tarabalka, Y.; Tilton, J.C.; Benediktsson, J.A.; Chanussot, J. A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 262-272.
19. Song, H.;Wang, Y. A spectral-spatial hyperspectral image classification based on algebraic multigrid methods and hierarchical segmentation. Remote Sens. 2016, 8, 296.
20. Fang, L.; Li, S.; Kang, X.; Benediktsson, J.A. Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model. IEEE Trans. Geosci. Remote Sens. 2015, 53, 4186-4201.
21. Kettig, R.L.; Landgrebe, D.A. Classification of multispectral image data by extraction and classification of homogeneous objects. IEEE Trans. Geosci. Electron. 1976, 14, 19-26.
22. Tarabalka, Y.; Chanussot, J.; Benediktsson, J.A. Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2010, 40, 1267-1279.
23. Tuia, D.; Camps-Valls, G.; Matasci, G.; Kanevski, M. Learning relevant image features with multiple kernel classification. IEEE Trans. Geosci. Remote Sens. 2010, 48, 3780-3791.
24. Li, J.; Marpu, P.R.; Plaza, A.; Bioucas-Dias, J.M.; Benediktsson, J.A. Generalized composite kernel framework for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2013, 51, 4816-4829.
25. Mathieu, F.; Jocelyn, C.; Atli, B.J. A spatial-spectral kernel-based approach for the classification of remote-sensing images. Pattern Recognit. 2012, 45, 381-392.
26. Kolmogorov, V.; Zabin, R. What energy functions can be minimized via graph cuts? IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 147-159.
27. Amini, A.A.; Weymouth, T.E.; Jain, R.C. Using dynamic programming for solving variational problems in vision. IEEE Trans. Pattern Anal. Mach. Intell. 1990, 12, 855-867.
28. Peng, B.; Zhang, L.; Zhang, D. A survey of graph theoretical approaches to image segmentation. Pattern Recognit. 2013, 46, 1020-1038.
29. Yu, X.; Niu, R.;Wang, Y.;Wu, K. Multiscale graph cut based classification of urban hyperspectral imagery. In Multispectral Image Processing and Pattern Recognition; SPIE: Bellingham, WA, USA, 2009; pp. 1-9.
30. Tarabalka, Y.; Rana, A. Graph-cut-based model for spectral-spatial classification of hyperspectral images. In Proceedings of the IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada, 13-18 July 2014.
31. Ma, L.; Ma, A.; Ju, C.; Li, X. Graph-based semi-supervise d learning for spectral-spatial hyperspectral image classification. Pattern Recognit. Lett. 2016.
32. Bai, J.; Xiang, S.; Pan, C. A graph-based classification method for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 2013, 51, 803-817.
33. Jia, S.; Zhang, X.; Li, Q. Spectral-spatial hyperspectral image classification using l1/2 regularized low-rank representation and sparse representation-based graph cuts. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2473-2484.
34. Damodaran, B.B.; Nidamanuri, R.R.; Tarabalka, Y. Dynamic ensemble selection approach for hyperspectral image classification with joint spectral and spatial information. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 2405-2417.
35. Tomasi, C.; Manduchi, R. Bilateral filtering for gray and color images. In Proceedings of the Sixth International Conference on Computer Vision, Bombay, India, 4-7 January 1998.
36. Shi, J.; Fowlkes, C.; Martin, D.; Sharon, E. Graph Based Image Segmentation Tutorial. Available online: http://www.cis.upenn.edu/~jshi/GraphTutorial/ (accessed on 27 June 2004).
37. Ford, D.R.; Fulkerson, D.R. Flows in Networks; Princeton University Press: Princeton, NJ, USA, 2010.
38. Boykov, Y.; Kolmogorov, V. An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 2004, 26, 1124-1137.
39. Boykov, Y.Y.; Jolly, M.-P. Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. In Proceedings of the Internation Conference on Computer Vision, Vancouver, BC, Canada, 13 July 2001.
40. Boykov, Y.; Funka-Lea, G. Graph cuts and efficient n-d image segmentation. Int. J. Comput. Vis. 2006, 70, 109-131.
41. Chang, C.-C.; Lin, C.-J. Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2011.
42. Wu, T.-F.; Lin, C.J.; Weng, R.C. Probability estimates for multiclass classification by pairwise coupling. J. Mach. Learn. Res. 2004, 5, 975-1005.
43. Vapnik, V.N. Statistical Learning Theory; Wiley: New York, NY, USA, 1998.
44. Paris, S.; Kornprobst, P.; Tumblin, J.; Durand, F. Bilateral filtering: Theory and applications. Found. Trends Comput. Graph. Vis. 2009, 4, 1-73.
45. Petschnigg, G.; Szeliski, R.; Agrawala, M.; Cohen, M.; Hoppe, H.; Toyama, K. Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 2004, 23, 664-672.
46. Rother, C.; Kolmogorov, V.; Blake, A. Grabcut: Interactive foreground extraction using iterated graph cuts. ACM Trans. Graph. 2004, 23, 309-314.
47. Kang, X.; Li, S.; Benediktsson, J.A. Spectral-spatial hyperspectral image classification with edge-preserving filtering. IEEE Trans. Geosci. Remote Sens. 2014, 52, 2666-2677.
48. Li, J.; Bioucas-Dias, J.M.; Plaza, A. Spectral-spatial classification of hyperspectral data using loopy belief propagation and active learning. IEEE Trans. Geosci. Remote Sens. 2013, 51, 844-856.
49. Bioucas-Dias, J.M.; Figueiredo, M. Logistic Regression via Variable Splitting and Augmented Lagrangian Tools; Instituto Superior Tecnico: Lisboa, Portugal, 2009.
50. Li, J.; Bioucas-Dias, J.M.; Plaza, A. Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Trans. Geosci. Remote Sens. 2011, 49, 3947-3960.
51. Li, J. The Source Codes of the MLR-LORSAL, LBP-AL and LORSAR-AL-MLL Methods. Available online: http://www.lx.it.pt/~jun (accessed on 8 September 2016).