Spectral-Spatial Semisupervised Hyperspectral Classification Using Adaptive Neighborhood

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 10, Issue 9, 2017, Pages 4183-4197

  Jamshidpour, Nasehe ^a   Safari, Abdolreza ^a   Homayouni, Saeid ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b UNIVERSITY OF OTTAWA   (Canada)

Author keywords

Adaptive neighborhood (AN); graph based SSL (GBSSL); hyperspectral classification; morphological flat zone area filtering; semisupervised learning (SSL); spatial spectral classifier

Indexed keywords

GRAPHIC METHODS; IMAGE ENHANCEMENT; PIXELS; SUPERVISED LEARNING;

ADAPTIVE NEIGHBORHOOD; GRAPH-BASED; HYPER-SPECTRAL CLASSIFICATION; SEMI-SUPERVISED LEARNING (SSL); SPECTRAL CLASSIFIER;

INFORMATION FILTERING;

ALGORITHM; DATA SET; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PIXEL; SPECTRAL ANALYSIS;

EID: 85030682767     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2017.2706190     Document Type: Article

References (37)

1. P. S. Thenkabail, J. G. Lyon, and A. Huete, Hyperspectral Remote Sensing of Vegetation. Boca Raton, FL, USA: CRC Press, 2016.
2. G. Camps-Valls, D. Tuia, L. Bruzzone, and J. A. Benediktsson, "Advances in hyperspectral image classification: Earth monitoring with statistical learning methods, " IEEE Signal Process. Mag., vol. 31, no. 1, pp. 45-54, Jan. 2014.
3. M. Fauvel, Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Advances in spectral-spatial classification of hyperspectral images, " Proc. IEEE, vol. 101, no. 3, pp. 652-675, Mar. 2013.
4. G. M. Foody, A. Mathur, C. Sanchez-Hernandez, and D. S. Boyd, "Training set size requirements for the classification of a specific class, " Remote Sens. Environ., vol. 104, pp. 1-14, 2006.
5. C. Persello and L. Bruzzone, "Active and semisupervised learning for the classification of remote sensing images, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 11, pp. 6937-6956, Nov. 2014.
6. B. M. Shahshahani and D. A. Landgrebe, "The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon, " IEEE Trans. Geosci. Remote Sens., vol. 32, no. 5, pp. 1087-1095, Sep. 1994.
7. A. N. Tikhonov and V. I. A. K. Arsenin, Solutions of Ill-Posed Problems. Palm Beach, FL, USA: V. H. Winston, 1977.
8. W. Zhao and S. Du, "Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4544-4554, Aug. 2016.
9. R. Luo, W. Liao, X. Huang, Y. Pi, and W. Philips, "Feature extraction of hyperspectral images with semisupervised graph learning, " IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 9, no. 9, pp. 4389-4399, Sep. 2016.
10. C. Persello and L. Bruzzone, "Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning, " IEEE Trans. Geosci. Remote Sens., vol. 54, no. 5, pp. 2615-2626, May 2016.
11. Q. Zhang, Y. Tian, Y. Yang, andC. Pan, "Automatic spatial-spectral feature selection for hyperspectral image via discriminative sparse multimodal learning, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 1, pp. 261-279, Jan. 2015.
12. X. Jia, B.-C. Kuo, andM. M. Crawford, "Feature mining for hyperspectral image classification, " Proc. IEEE, vol. 101, no. 3, pp. 676-697, Mar. 2013.
13. D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Munoz-Mari, "A survey of active learning algorithms for supervised remote sensing image classification, " IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 606-617, Jun. 2011.
14. O. Chapelle, B. Schokopf, and A. Zien, Semi-Supervised Learning (Adaptive Computation and Machine Learning), Cambridge, MA, USA: MIT Press, 2006.
15. G. Camps-Valls, T. Bandos Marsheva, and D. Zhou, "Semi-supervised graph-based hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 10, pp. 3044-3054, Oct. 2007.
16. Y. Gu and K. Feng, "L1-graph semisupervised learning for hyperspectral image classification, " in Proc. 2012 IEEE Int. Geosci. Remote Sens. Symp., 2012, pp. 1401-1404.
17. L. Ma, M. M. Crawford, X. Yang, and Y. Guo, "Local-manifold-learningbased graph construction for semisupervised hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2832-2844, May 2015.
18. Y. Shao, N. Sang, C. Gao, and L. Ma, "Probabilistic class structure regularized sparse representation graph for semi-supervised hyperspectral image classification, " Pattern Recog., vol. 63, pp. 102-114, 2017.
19. L. Ma, A. Ma, C. Ju, and X. Li, "Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification, " Pattern Recog. Lett., vol. 83, pp. 133-142, 2016.
20. R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li, "Spectral-spatial constraint hyperspectral image classification, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1811-1824, Mar. 2014.
21. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "A spatial-spectral kernel-based approach for the classification of remote-sensing images, " Pattern Recog., vol. 45, pp. 381-392, 2012.
22. Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton, "Multiple spectral-spatial classification approach for hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4122-4132, Nov. 2010.
23. Z. Chen and B. Wang, "Spectral-spatial classification based on affinity scoring for hyperspectral imagery, " IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 9, no. 6, pp. 2305-2320, Jun. 2016.
24. Z. Zhang, E. Pasolli, M. M. Crawford, and J. C. Tilton, "An active learning framework for hyperspectral image classification using hierarchical segmentation, " IEEE J. Sel. Topics Appl. Earth Obs. Remote Sens., vol. 9, no. 2, pp. 640-654, Feb. 2016.
25. P. Salembier and J. Serra, "Flat zones filtering, connected operators, and filters by reconstruction, " IEEE Trans. Image Process., vol. 4, no. 8, pp. 1153-1160, Aug. 1995.
26. M. Dalla Mura, J. N. A. Benediktsson, B. Waske, and L. Bruzzone, "Morphological attribute profiles for the analysis of very high resolution images, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747-3762, Oct. 2010.
27. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 11, pp. 3804-3814, Nov. 2008.
28. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "Adaptive pixel neighborhood definition for the classification of hyperspectral images with support vector machines and composite kernel, " in Proc. 2008 15th IEEE Int. Conf. Image Process., 2008, pp. 1884-1887.
29. P. Soille, "Beyond self-duality in morphological image analysis, " Image Vis. Comput., vol. 23, pp. 249-257, 2005.
30. D. Brunner and P. Soille, "Iterative area filtering of multichannel images, " Image Vis. Comput., vol. 25, pp. 1352-1364, 2007.
31. P. Soille, "Constrained connectivity for hierarchical image partitioning and simplification, " IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 7, pp. 1132-1145, Jul. 2008.
32. J. Weber and S. Lefèvre, "Fast quasi-flat zones filtering using area threshold and region merging, " J. Vis. Commun. Image Represent., vol. 24, pp. 397-409, 2013.
33. A. J. Plaza and C.-I. Chang, High Performance Computing in Remote Sensing. Boca Raton, FL, USA: CRC Press, 2007.
34. M. Fornasier, S. Peter, H. Rauhut, and S. Worm, "Conjugate gradient acceleration of iteratively re-weighted least squares methods, " Comput. Optimiz. Appl., vol. 65, pp. 205-259, 2016.
35. J. Nocedal and S. Wright, Numerical Optimization. New York, NY, USA: Springer, 2006.
36. C.-C. A. L. Chang and C. Jen, "LIBSVM: A library for support vector machines, " ACM Trans. Intell. Syst. Technol., vol. 2, 2011, Art. no. 27.
37. G. M. Foody, "Thematic map comparison, " Photogram. Eng. Remote Sens., vol. 70, pp. 627-633, 2004.