Hyperspectral classification via deep networks and superpixel segmentation

International Journal of Remote Sensing

Volumn 36, Issue 13, 2015, Pages 3459-3482

  Liu, Yazhou ^a   Cao, Guo ^a   Sun, Quansen ^a   Siegel, Mel ^b  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PIXELS; SPECTROSCOPY; SUPERPIXELS;

ACCURACY OF CLASSIFICATIONS; CLASSIFICATION ACCURACY; FEATURE REPRESENTATION; HYPER-SPECTRAL CLASSIFICATION; NEIGHBOURHOOD PIXELS; SPATIAL CONSISTENCY; SPECTRAL CLASSIFICATION; SUPERPIXEL SEGMENTATIONS;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; IMAGE CLASSIFICATION; MULTISPECTRAL IMAGE; PIXEL; SEGMENTATION; SPECTRAL RESOLUTION;

EID: 84937140953     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/01431161.2015.1055607     Document Type: Article

References (61)

1. R.Achanta,, A.Shaji, K.Smith, A.Lucchi, P.Fua, and S.Süsstrunk. 2012. “SLIC Superpixels Compared to State-of-the-art Superpixel Methods.” IEEE Transactions on Pattern Analysis and Machine Intelligence 34 (11): 2274–2282. doi:10.1109/TPAMI.2012.120.
2. B.Antoine,, X.Glorot, J.Weston, and Y.Bengio. 2012. “Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing.” In International Conference on Artificial Intelligence and Statistics, edited by B.Schölkopf, M.Girolami and N.Lawrence. Sardinia: ICAIS.
3. R.Archibald,, and G.Fann. 2007. “Feature Selection and Classification of Hyperspectral Images with Support Vector Machines.” IEEE Geoscience and Remote Sensing Letters 4 (4): 674–677. doi:10.1109/LGRS.2007.905116.
4. I.Arel,, D.C.Rose, and T.P.Karnowski. 2010. “Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier].” IEEE Computational Intelligence Magazine 5 (4): 13–18. doi:10.1109/MCI.2010.938364.
5. J.A.Benediktsson,, J.A.Palmason, and J.R.Sveinsson. 2005. “Classification of Hyperspectral Data from Urban Areas Based on Extended Morphological Profiles.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 480–491. doi:10.1109/TGRS.2004.842478.
6. Y.Bengio, 2009. “Learning Deep Architectures for AI.” Foundations and Trends in Machine Learning 2: 1–127. doi:10.1561/2200000006.
7. Y.Bengio,, A.Courville, and P.Vincent. 2013. “Representation Learning: A Review and New Perspectives.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1798–1828. doi:10.1109/TPAMI.2013.50.
8. Y.Bengio,, P.Lamblin, D.Popovici, and H.Larochelle. 2007. “Greedy Layer-Wise Training of Deep Networks.” In Advances in Neural Information Processing Systems, edited by B.Schölkopf, J.C.Platt and T.Hofmann. Cambridge, MA: MIT Press.
9. J.Bergstra,, O.Breuleux, F.Bastien, P.Lamblin, R.Pascanu, G.Desjardins, J.Turian, D.Warde-Farley, and Y.Bengio. 2010. “Theano: a CPU and GPU Math Expression Compiler.” In Python for Scientific Computing Conference, edited by S.van der Walt and J.Millman, SciPy, Austin, TX, June 30–July 3.
10. L.Bottou, 2010. “Large-Scale Machine Learning with Stochastic Gradient Descent.” In Proceedings of COMPSTAT, edited by Y.Lechevallier and G.Saporta, 177–186. Paris: Physica-Verlag HD.
11. G.Camps-Valls,, T.Bandos Marsheva, and D.Zhou. 2007. “Semi-supervised Graph-based Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 45 (10): 3044–3054. doi:10.1109/TGRS.2007.895416.
12. G.Camps-Valls,, and L.Bruzzone. 2005. “Kernel-based Methods for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 43 (6): 1351–1362. doi:10.1109/TGRS.2005.846154.
13. G.Camps-Valls,, J.Mooij, and B.Scholkopf. 2010. “Remote Sensing Feature Selection by Kernel Dependence Measures.” IEEE Geoscience and Remote Sensing Letters 7 (3): 587–591. doi:10.1109/LGRS.2010.2041896.
14. G.Camps-Valls,, D.Tuia, L.Bruzzone, and J.A.Benediktsson. 2014. “Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods.” IEEE Signal Processing Magazine 31 (1): 45–54. doi:10.1109/MSP.2013.2279179.
15. C.-I.Chang, 2007. Hyperspectral Data Exploitation: Theory and Applications. Hoboken, NJ: Wiley-Interscience.
16. Y.Chen,, Z.Lin, X.Zhao, G.Wang, and Y.Gu. 2014. “Deep Learning-Based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107. doi:10.1109/JSTARS.2014.2329330.
17. D.Cireşan,, U.Meier, J.Masci, and J.Schmidhuber. 2012. “Multi-Column Deep Neural Network for Traffic Sign Classification.” Neural Networks 32: 333–338. doi:10.1016/j.neunet.2012.02.023.
18. N.Dalal,, and B.Triggs. 2005. “Histograms of Oriented Gradients for Human Detection.” In Computer Vision and Pattern Recognition, edited by C.Schmid, S.Soatto and C.Tomasi. San Diego, CA: IEEE Computer Society.
19. O.Eches,, N.Dobigeon, C.Mailhes, and J.-Y.Tourneret. 2010. “Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery.” IEEE Transactions on Image Processing 19 (6): 1403–1413. doi:10.1109/TIP.2010.2042993.
20. C.Farabet,, C.Couprie, L.Najman, and Y.LeCun. 2013. “Learning Hierarchical Features for Scene Labeling.” IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (8): 1915–1929. doi:10.1109/TPAMI.2012.231.
21. M.Fauvel,, J.Chanussot, and J.A.Benediktsson. 2009. “Kernel Principal Component Analysis for the Classification of Hyperspectral Remote Sensing Data over Urban Areas.” EURASIP Journal on Advances in Signal Processing 2009: 1–15. doi:10.1155/2009/783194.
22. M.Fauvel,, Y.Tarabalka, J.A.Benediktsson, J.Chanussot, and J.C.Tilton. 2013. “Advances in Spectral Spatial Classification of Hyperspectral Images.” Proceedings of the IEEE 101 (3): 652–675. doi:10.1109/JPROC.2012.2197589.
23. X.Glorot,, and Y.Bengio. 2010. “Understanding the Difficulty of Training Deep Feedforward Neural Networks.” In International Conference on Artificial Intelligence and Statistics, edited by N.Lawrence, Y.W.Teh and M.Titterington, 249–256. Sardinia: ICAIS.
24. M.A.Goodale,, and A.D.Milner. 1992. “Separate Visual Pathways for Perception and Action.” Trends in Neurosciences 15 (1): 20–25. doi:10.1016/0166-2236(92)90344-8.
25. Y.Gu,, Y.Liu, and Y.Zhang. 2008. “A Selective KPCA Algorithm Based on High-Order Statistics for Anomaly Detection in Hyperspectral Imagery.” IEEE Geoscience and Remote Sensing Letters 5 (1): 43–47. doi:10.1109/LGRS.2007.907304.
26. B.Guo,, S.R.Gunn, R.I.Damper, and J.D.B.Nelson. 2008. “Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification.” IEEE Transactions on Image Processing 17 (4): 622–629. doi:10.1109/TIP.2008.918955.
27. B.Guo,, S.R.Gunn, R.I.Damper, and J.D.B.Nelson. 2006. “Band Selection for Hyperspectral Image Classification Using Mutual Information.” IEEE Geoscience and Remote Sensing Letters 3 (4): 522–526. doi:10.1109/LGRS.2006.878240.
28. J.Ham,, Y.Chen, M.M.Crawford, and J.Ghosh. 2005. “Investigation of the Random Forest Framework for Classification of Hyperspectral Data.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 492–501. doi:10.1109/TGRS.2004.842481.
29. A.Hyvärinen,, and E.Oja. 2000. “Independent Component Analysis: Algorithms and Applications.” Neural Networks 13 (4–5): 411–430. doi:10.1016/S0893-6080(00)00026-5.
30. E.Izquierdo-Verdiguier,, L.Gomez-Chova, L.Bruzzone, and G.Camps-Valls. 2014. “Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis.” IEEE Transactions on Geoscience and Remote Sensing 52 (9): 5567–5578. doi:10.1109/TGRS.2013.2290372.
31. R.Ji,, Y.Gao, R.Hong, Q.Liu, D.Tao, and X.Li. 2013. “Spectral-Spatial Constraint Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 52 (3): 1811–1824.
32. H.Jiao,, Y.Zhong, L.Zhang, and P.Li. 2011. “Unsupervised Remote Sensing Image Classification Using an Artificial DNA Computing.” In International Conference on Natural Computation, edited by Y.Ding, H.Wang, N.Xiong, K.Hao and L.Wang, 1341–1345. Shanghai: ICNC.
33. K.Kavukcuoglu,, P.Sermanet, Y.Lan Boureau, K.Gregor, M.Mathieu, and Y.LeCun. 2010. “Learning Convolutional Feature Hierachies for Visual Recognition.” In Advances in Neural Information Processing Systems, edited by J.D.Lafferty, C.K.I.Williams, J.Shawe-Taylor, R.S.Zemel and A.Culotta. Vancouver, BC: NIPS.
34. A.Krizhevsky,, I.Sutskever, and G.E.Hinton. 2012. “ImageNet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, edited by F.Pereira, C.J.C.Burges, L.Bottou and K.Q.Weinberger. Lake Tahoe: NIPS.
35. B.-C.Kuo,, C.-H.Li, and J.-M.Yang. 2009. “Kernel Nonparametric Weighted Feature Extraction for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 47 (4): 1139–1155. doi:10.1109/TGRS.2008.2008308.
36. D.Landgrebe, 2002. “Hyperspectral Image Data Analysis.” IEEE Signal Processing Magazine 19 (1): 17–28. doi:10.1109/79.974718.
37. H.Lee,, C.Ekanadham, and A.Y.Ng. 2008. “Sparse Deep Belief Net Model for Visual Area V2.” In Advances in Neural Information Processing Systems, edited by D.Koller, D.Schuurmans, Y.Bengio, and L.Bottou. Vancouver, BC: NIPS.
38. J.Li,, P.R.Reddy Marpu, A.Plaza, J.M.Bioucas-Dias, and J.A.Atli Benediktsson. 2013. “Generalized Composite Kernel Framework for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 51 (9): 4816–4829. doi:10.1109/TGRS.2012.2230268.
39. S.Li,, H.Wu, D.Wan, and J.Zhu. 2011. “An Effective Feature Selection Method for Hyperspectral Image Classification Based on Genetic Algorithm and Support Vector Machine.” Knowledge-Based Systems 24 (1): 40–48. doi:10.1016/j.knosys.2010.07.003.
40. S.Li,, X.Jia, and B.Zhang. 2013. “Superpixel-based Markov Random Field for Classification of Hyperspectral Images.” In IEEE International Geoscience and Remote Sensing Symposium, edited by P.Woodgate and S.Jones, 3491–3494. Melbourne: IEEE GRSS.
41. Z.Lin,, Y.Chen, X.Zhao, and G.Wang. 2013. “Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders.” In International Conference on Information, Communications and Signal Processing, edited by P.H.J.Chong, J.Pau-Choo Chung and Z.Lin. Tainan: ICICS.
42. T.Liu,, L.Zhang, P.Li, and H.Lin. 2012. “Remotely Sensed Image Retrieval Based on Region-Level Semantic Mining.” EURASIP Journal on Image and Video Processing 4:1–11.
43. M.Marconcini,, G.Camps-Valls, and L.Bruzzone. 2009. “A Composite Semisupervised SVM for Classification of Hyperspectral Images.” IEEE Geoscience and Remote Sensing Letters 6 (2): 234–238. doi:10.1109/LGRS.2008.2009324.
44. A.Martnez-Uso,, F.Pla, J.M.Sotoca, and P.Garca-Sevilla. 2007. “Clustering-Based Hyperspectral Band Selection Using Information Measures.” IEEE Transactions on Geoscience and Remote Sensing 45 (12): 4158–4171. doi:10.1109/TGRS.2007.904951.
45. F.Melgani,, and L.Bruzzone. 2004. “Classification of Hyperspectral Remote Sensing Images with Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42 (8): 1778–1790. doi:10.1109/TGRS.2004.831865.
46. M.D.Mura,, J.A.Benediktsson, B.Waske, and L.Bruzzone. 2010b. “Morphological Attribute Profiles for the Analysis of Very High Resolution Images.” IEEE Transactions on Geoscience and Remote Sensing 48 (10): 3747–3762. doi:10.1109/TGRS.2010.2048116.
47. M.D.Mura,, J.A.Benediktsson, B.Waske, and L.Bruzzone. 2010a. “Extended Profiles with Morphological Attribute Filters for the Analysis of Hyperspectral Data.” International Journal of Remote Sensing 31 (22): 5975–5991. doi:10.1080/01431161.2010.512425.
48. M.Pesaresi,, and J.A.Benediktsson. 2001. “A New Approach for The Morphological Segmentation of High-Resolution Satellite Imagery.” IEEE Transactions on Geoscience and Remote Sensing 39 (2): 309–320. doi:10.1109/36.905239.
49. F.Ratle,, G.Camps-Valls, and J.Weston. 2010. “Semisupervised Neural Networks for Efficient Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 48 (5): 2271–2282. doi:10.1109/TGRS.2009.2037898.
50. J.A.Richards, 2006. Remote Sensing Digital Image Analysis: An Introduction. New York: Springer-Verlag.
51. R.Socher,, E.H.Huang, J.Pennington, A.Y.Ng, and C.D.Manning. 2011. “Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection.” In Advances in Neural Information Processing Systems, edited by J.Shawe-Taylor, R.S.Zemel, P.L.Bartlett, F.Pereira and K.Q.Weinberger. Granada: NIPS.
52. R.Socher,, J.Pennington, E.H.Huang, A.Y.Ng, and C.D.Manning. 2011. “Semi-supervised Recursive Autoencoders for Predicting Sentiment Distributions.” In Conference on Empirical Methods in Natural Language Processing, edited by A.Belz, R.Evans, A.Gatt and K.Striegnitz, 151–161. Edinburgh: ACL.
53. P.Soille, 2003. Morphological Image Analysis, Principles and Applications. New York: Springer-Verlag.
54. B.Song,, J.Li, M.D.Mura, P.Li, A.Plaza, J.M.Bioucas- Dias, J.Atli Benediktsson, and J.Chanussot. 2013. “Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles.” IEEE Transactions on Geoscience and Remote Sensing 52 (8): 5122–5136. doi:10.1109/TGRS.2013.2286953.
55. C.Tao,, Y.Tang, C.Fan, and Z.Zou. 2014. “Hyperspectral Imagery Classification Based on Rotation-Invariant Spectral–Spatial Feature.” IEEE Geoscience and Remote Sensing Letters 11 (5): 980–984. doi:10.1109/LGRS.2013.2284007.
56. P.Vincent,, H.Larochelle, Y.Bengio, and P.-A.Manzagol. 2008. “Extracting and Composing Robust Features with Denoising Autoencoders.” In International Conference on Machine Learning, edited by W.Cohen, A.McCallum and S.Roweis. Helsinki: ACM.
57. P.Vincent,, H.Larochelle, I.Lajoie, Y.Bengio, and P.-A.Manzagol. 2010. “Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion.” Journal of Machine Learning Research 11: 3371–3408.
58. P.Viola,, and M.J.Jones. 2004. “Robust Real-Time Face Detection.” International Journal of Computer Vision 57 (2): 137–154. doi:10.1023/B:VISI.0000013087.49260.fb.
59. J.Wang,, and C.-I.Chang. 2006. “Independent Component Analysis based DimensionalityReduction with Applications in Hyperspectral Image Analysis.” IEEE Transactions on Geoscience and Remote Sensing 44 (6): 1586–1600. doi:10.1109/TGRS.2005.863297.
60. B.Yegnanarayana, 2009. Artificial Neural Networks. New Delhi: PHI Learning.
61. P.Zhong,, and R.Wang. 2010. “Learning Conditional Random Fields for Classification of Hyperspectral Images.” IEEE Transactions on Image Processing 19 (7): 1890–1907. doi:10.1109/TIP.2010.2045034.