Support Tensor Machines for Classification of Hyperspectral Remote Sensing Imagery

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 6, 2016, Pages 3248-3264

  Guo, Xian ^a   Huang, Xin ^b   Zhang, Lefei ^b,c   Zhang, Liangpei ^b   Plaza, Antonio ^d   Benediktsson, Jon Atli ^e  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b WUHAN UNIVERSITY   (China)

^c HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

^d UNIVERSITY OF EXTREMADURA   (Spain)

^e UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Classification; dimensionality reduction; feature extraction; hyperspectral; support tensor machine (STM); support vector machine (SVM); tensor

Indexed keywords

AGRICULTURAL MACHINERY; CLUSTERING ALGORITHMS; EXTRACTION; FEATURE EXTRACTION; IMAGE CLASSIFICATION; INDEPENDENT COMPONENT ANALYSIS; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SPACE OPTICS; SPECTROSCOPY; SUPPORT VECTOR MACHINES; TENSORS;

DIMENSIONALITY REDUCTION; HYPERSPECTRAL; HYPERSPECTRAL REMOTE SENSING; MULTILINEAR PRINCIPAL COMPONENT ANALYSIS (MPCA); REMOTE SENSING IMAGE CLASSIFICATION; SUPPORT TENSOR MACHINE (STM); SUPPORT TENSOR MACHINES; SUPPORT VECTOR MACHINE (SVMS);

CLASSIFICATION (OF INFORMATION);

DATA ASSIMILATION; DATA SET; EXPERIMENTAL STUDY; IMAGE ANALYSIS; IMAGE CLASSIFICATION; MODEL VALIDATION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINE;

EID: 84955602136     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2514404     Document Type: Article

References (58)

1. 6. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, no. 1, pp. 110-122, Sep. 2009.
2. L. G. Olmanson, P. L. Brezonik, M. E. Bauer, "Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi river and its tributaries in Minnesota," Remote Sens. Environ., vol. 130, pp. 254-265, Mar. 2013.
3. J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis, 4th ed. Berlin, Germany: Springer-Verlag, 2006.
4. M. S. Moran, Y. Inoue, E. M. Barnes, "Opportunities and limitations for image-based remote sensing in precision crop management," Remote Sens. Environ., vol. 61, no. 3, pp. 319-346, Sep. 1997.
5. J. Stuckens, P. R. Coppin, M. E. Bauer, "Integrating contextual information with per-pixel classification for improved land cover classification," Remote Sens. Environ., vol. 71, no. 3, pp. 282-296, Mar. 2000.
6. F. Yuan, K. E. Sawaya, B. C. Loeffelholz, M. E. Bauer, "Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by Multitemporal Landsat remote sensing," Remote Sens. Environ., vol. 98, no. 2/3, pp. 317-328, Oct. 2005.
7. R. A. Schowengerdt, Remote Sensing: Models and Methods for Image Processing, 3rd ed. Burlington, VT, USA: Academic, 2007.
8. A. Brenning, "Benchmarking classifiers to optimally integrate terrain analysis and multispectral remote sensing in automatic rock glacier detection," Remote Sens. Environ., vol. 113, no. 1, pp. 239-247, Jan. 2009.
9. J. Ham, C. Yangchi, M. M. Crawford, J. Ghosh, "Investigation of the random forest framework for classification of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 492-501, Mar. 2005.
10. R. L. Lawrence, S. D. Wood, R. L. Sheley, "Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (randomForest)," Remote Sens. Environ., vol. 100, no. 3, pp. 356-362, Feb. 2006.
11. J. B. Adams et al., "Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon," Remote Sens. Environ., vol. 52, no. 2, pp. 137-154, May 1995.
12. U. Heiden, K. Segl, S. Roessner, H. Kaufmann, "Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data," Remote Sens. Environ., vol. 111, no. 4, pp. 537-552, Dec. 2007.
13. D. Vikhamar and R. Solberg, "Subpixel mapping of snow cover in forests by optical remote sensing," Remote Sens. Environ., vol. 84, no. 1, pp. 69-82, Jan. 2003.
14. A. M. Filippi and J. R. Jensen, "Fuzzy learning vector quantization for hyperspectral coastal vegetation classification," Remote Sens. Environ., vol. 100, no. 4, pp. 512-530, Feb. 2006.
15. S. Delalieux et al., "Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers," Remote Sens. Environ., vol. 126, pp. 222-231, Nov. 2012.
16. J. C. Harsanyi and C.-I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, no. 4, pp. 779-785, Jul. 1994.
17. J. A. Benediktsson and I. Kanellopoulos, "Classification of multisource and hyperspectral data based on decision fusion," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 3, pp. 1367-1377, May 1999.
18. Y. Chen, N. M. Nasrabadi, T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
19. A. Banerjee, P. Burlina, C. Diehl, "A support vector method for anomaly detection in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 8, pp. 2282-2291, Aug. 2006.
20. T. S. Furey et al., "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, vol. 16, no. 10, pp. 906-914, Oct. 2000.
21. A. K. Jain, R. P. W. Duin, M. Jianchang, "Statistical pattern recognition: A review," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 4-37, Jan. 2000.
22. G. Mountrakis, J. Im, C. Ogole, "Support vector machines in remote sensing: A review," ISPRS J. Photogramm., vol. 66, pp. 247-259, May 2011.
23. G. M. Foody and A. Mathur, "Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification," Remote Sens. Environ., vol. 93, no. 1/2, pp. 107-117, Oct. 2004.
24. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 8, pp. 1778-1790, Aug. 2004.
25. X. Huang and L. Zhang, "An SVM ensemble approach combining spectral, structural, semantic features for the classification of highresolution remotely sensed imagery," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 257-272, Jan. 2013.
26. A. Okujeni, S. van der Linden, L. Tits, B. Somers, P. Hostert, "Support vector regression and synthetically mixed training data for quantifying urban land cover," Remote Sens. Environ., vol. 137, pp. 184-197, Oct. 2013.
27. D. A. Landgrebe, Signal Theory Methods in Multispectral Remote Sensing. New York, NY, USA: Wiley, 2003.
28. W. Li, S. Prasad, J. E. Fowler, L. M. Bruce, "Locality-preserving dimensionality reduction and classification for hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 4, pp. 1185-1198, Apr. 2012.
29. L. Bruzzone, M. Chi, M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
30. Y. Tarabalka, J. A. Benediktsson, J. Chanussot, "Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
31. M. Fauvel, J. A. Benediktsson, J. Chanussot, 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.
32. M. Khodadadzadeh et al., "Spectral-spatial classification of hyperspectral data using local and global probabilities for mixed pixel characterization," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 10, pp. 6298-6314, Oct. 2014.
33. G. Camps-Valls, L. Gomez-Chova, J. Munõz-Marí, J. Vila-Francés, J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 3, no. 1, pp. 93-97, Jan. 2006.
34. D. Tuia, E. Pasolli, W. J. Emery, "Using active learning to adapt remote sensing image classifiers," Remote Sens. Environ., vol. 115, no. 9, pp. 2232-2242, Sep. 2011.
35. D. Tuia, M. Volpi, L. Copa, M. Kanevski, 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.
36. D. Tao, X. Li, X. Wu, W. Hu, S. J. Maybank, "Supervised tensor learning," Knowl. Inf. Syst., vol. 13, no. 1, pp. 1-42, 2007.
37. Z. Zhang and T.W. S. Chow, "Maximum margin multisurface support tensor machines with application to image classification and segmentation," Expert Syst. Appl., vol. 39, no. 1, pp. 849-860, Jan. 2012.
38. Z. Hao, L. He, B. Chen, X. Yang, "A linear support higher-order tensor machine for classification," IEEE Trans. Image Process., vol. 22, no. 7, pp. 2911-2920, Jul. 2013.
39. I. Kotsia, W. Guo, I. Patras, "Higher rank support tensor machines for visual recognition," Pattern Recognit., vol. 45, no. 12, pp. 4192-4203, Dec. 2012.
40. H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "MPCA: Multilinear principal component analysis of tensor objects," IEEE Trans. Neural Netw., vol. 19, no. 1, pp. 18-39, Jan. 2008.
41. L. D. Lathauwer, "Signal processing based on multilinear algebra," Ph.D. dissertation, Departement Elektrotechniek, Katholike Univ. Leuven, Leuven, Belgium, 1997.
42. L. D. Lathauwer, B. D. Moor, J. Vandewalle, "A multilinear singular value decomposition," SIAM J. Matrix Anal. Appl., vol. 21, no. 4, pp. 1253-1278, Apr. 2000.
43. T. G. Kolda and B. W. Bader, "Tensor decompositions and applications," SIAM Rev., vol. 51, no. 3, pp. 455-500, Sep. 2009.
44. H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "A survey of multilinear subspace learning for tensor data," Pattern Recognit., vol. 44, no. 7, pp. 1540-1551, Jul. 2011.
45. C. Huang, L. S. Davis, J. R. G. Townshend, "An assessment of support vector machines for land cover classification," Int. J. Remote Sens., vol. 23, no. 5, pp. 725-749, Jan. 2002.
46. J. Bezdek and R. Hathaway, "Some notes on alternating optimization," in Proc. AFSS Int. Conf. Fuzzy Syst., London, U.K., 2002, pp. 288-300.
47. C. Cortes and V. Vapnik, "Support vector networks," Mach. Learn., vol. 20, pp. 273-297, 1995.
48. D. Xu et al., "Concurrent subspaces analysis," in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., San Diego, CA, USA, 2005, vol. 2, pp. 203-208.
49. A. Karami, M. Yazdi, A. Z. Asli, "Noise reduction of hyperspectral images using kernel non-negative Tucker decomposition," IEEE J. Sel. Topics Signal Process., vol. 5, no. 3, pp. 487-493, Jun. 2011.
50. D. Letexier and S. Bourennane, "Noise removal from hyperspectral images by multidimensional filtering," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2061-2069, Jul. 2008.
51. N. Renard and S. Bourennane, "Dimensionality reduction based on tensor modeling for classification methods," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 4, pp. 1123-1131, Apr. 2009.
52. M. A. O. Vasilescu and D. Terzopoulos, "Multilinear subspace analysis of image ensembles," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Madison, WI, USA, 2003, vol. 2, pp. 93-99.
53. H. Lu, K. N. Plataniotis, A. N. Venetsanopoulos, "Multilinear principal component analysis of tensor objects for recognition," in Proc. IEEE Int. Conf. Pattern Recognit., Hongkong, 2006, vol. 2, pp. 776-779.
54. D. Xu et al., "Human gait recognition with matrix representation," IEEE Trans. Circuits Syst. Video Technol., vol. 16, no. 7, pp. 896-903, Jul. 2006.
55. S. Yan et al., "Multilinear discriminant analysis for face recognition," IEEE Trans. Image Process., vol. 16, no. 1, pp. 212-220, Jan. 2007.
56. D. Tao, X. Li, X. Wu, S. J. Maybank, "General tensor discriminant analysis and gabor features for gait recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 10, pp. 1700-1715, Oct. 2007.
57. F. Tsai and J.-S. Lai, "Feature extraction of hyperspectral image cubes using three-dimensional gray-level cooccurrence," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 6, pp. 3504-3513, Jun. 2013.
58. X. Huang, Q. Lu, L. Zhang, "A multi-index learning approach for classification of high-resolution remotely sensed images over urban areas," ISPRS J. Photogramm., vol. 90, pp. 36-48, Apr. 2014.