A novel semi-supervised hyperspectral image classification approach based on spatial neighborhood information and classifier combination

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 105, Issue , 2015, Pages 19-29

  Tan, Kun ^a   Hu, Jun ^a   Li, Jun ^b   Du, Peijun ^c  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b SUN YAT SEN UNIVERSITY   (China)

^c NANJING UNIVERSITY   (China)

Author keywords

Classifier fusion; Hyperspectral images; K nearest neighbor (KNN); Multinomial logistic regression (MLR); Semi supervised classification; Spatial neighborhood information

Indexed keywords

CLASSIFICATION (OF INFORMATION); FACE RECOGNITION; INDEPENDENT COMPONENT ANALYSIS; LEARNING ALGORITHMS; MOTION COMPENSATION; NEAREST NEIGHBOR SEARCH; REGRESSION ANALYSIS; SPECTROSCOPY; SUPERVISED LEARNING;

CLASSIFIER FUSION; HYPER-SPECTRAL IMAGES; K NEAREST NEIGHBOR (KNN); MULTINOMIAL LOGISTIC REGRESSION; SEMI-SUPERVISED CLASSIFICATION; SPATIAL NEIGHBORHOODS;

IMAGE CLASSIFICATION;

ALGORITHM; DATA SET; IMAGE CLASSIFICATION; NEIGHBORHOOD; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; SPECTRAL ANALYSIS;

EID: 84927624381     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2015.03.006     Document Type: Article

References (45)

1. Böhning D. Multinomial logistic regression algorithm. Ann. Inst. Stat. Math. 1992, 44(1):197-200.
2. Baassou B., He M., Farid M.I., Mei S. Hyperspectral image classification based on iterative Support Vector Machine by integrating spatial-spectral information. IGARSS 2013, 1023-1026.
3. 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(3):480-491.
4. Bioucas-Dias J., Plaza A., Camps-Valls G., Scheunders P., Nasrabadi N., Chanussot J. Hyperspectral remote sensing data analysis and future challenges. Geosci. Remote Sens. Mag. IEEE 2013, 1(2):6-36.
5. Bruce L.M., Koger C.H., Li J. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction. IEEE Trans. Geosci. Remote Sens. 2002, 40(10):2331-2338.
6. Camps-Valls G., Bruzzone L. Kernel-based methods for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2005, 43(6):1351-1362.
7. Camps-Valls G., Gomez-Chova L., Munoz-Mari J., Rojo-Alvarez J.L., Martinez-Ramon M. Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans. Geosci. Remote Sens. 2008, 46(6):1822-1835.
8. 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(1):93-97.
9. Chapelle O., Schölkopf B., Zien A. Semi-supervised learning 2006, MIT Press Cambridge.
10. Dópido I., Li J., Marpu P.R., Plaza A., Bioucas-Dias J.M., Benediktsson J.A. Semi-supervised self-learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2013, 51(7):4032-4044.
11. Demir B., Minello L., Bruzzone L. An effective strategy to reduce the labeling cost in the definition of training sets by active learning. Geosci. Remote Sens. Lett. IEEE 2014, 11(1):79-83.
12. Di, W., Crawford, M.M., 2010. Locally consistent graph regularization based active learning for hyperspectral image classification. In: Second Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2010. IEEE, pp. 1-4.
13. Di W., Crawford M.M. Active learning via multi-view and local proximity co-regularization for hyperspectral image classification. IEEE J. Select. Topics Signal Process. 2011, 5(3):618-628.
14. Di W., Crawford M.M. View generation for multiview maximum disagreement based active learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2012, 50(5):1942-1954.
15. Du, H., Qi, H., Wang, X., Ramanath, R., Snyder, W.E., 2003. Band selection using independent component analysis for hyperspectral image processing, In: Applied Imagery Pattern Recognition Workshop, 2003. Proceedings. 32nd. IEEE, pp. 93-98.
16. Du P., Xia J., Zhang W., Tan K., Liu Y., Liu S. Multiple classifier system for remote sensing image classification: a review. Sensors 2012, 12(4):4764-4792.
17. Grandvalet, Y., Bengio, Y., 2005. Semi-supervised learning by entropy minimization.
18. Green R.O., Eastwood M.L., Sarture C.M., Chrien T.G., Aronsson M., Chippendale B.J., Faust J.A., Pavri B.E., Chovit C.J., Solis M. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sens. Environ. 1998, 65(3):227-248.
19. Jiao H., Zhong Y., Zhang L. Artificial DNA computing-based spectral encoding and matching algorithm for hyperspectral remote sensing data. IEEE Trans. Geosci. Remote Sens. 2012, 50(10):4085-4104.
20. Jin, G., Raich, R., Miller, D.J., 2013. A generative semi-supervised model for multi-view learning when some views are label-free, In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2013. IEEE, pp. 3302-3306.
21. Joachims T. Transductive inference for text classification using support vector machines. ICML 1999, 200-209.
22. Li J., Bioucas-Dias J.M., Plaza A. Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Trans. Geosci. Remote Sens. 2010, 48(11):4085-4098.
23. 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(10):3947-3960.
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(9):4816-4829.
25. Li, L., Mao, T., Huang, D., 2005. Extracting location names from Chinese texts based on SVM and KNN, In: Proceedings of 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE'05. IEEE, pp. 371-375.
26. Luo, T., Kramer, K., Samson, S., Remsen, A., Goldgof, D., Hall, L., Hopkins, T., 2004. Active learning to recognize multiple types of plankton, In: Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, pp. 478-481.
27. Plaza A., Benediktsson J.A., Boardman J.W., Brazile J., Bruzzone L., Camps-Valls G., Chanussot J., Fauvel M., Gamba P., Gualtieri A., Marconcini M., Tilton J.C., Trianni G. Recent advances in techniques for hyperspectral image processing. Remote Sens. Environ. 2009, 113(Supplement 1):S110-S122.
28. Richards, J.A., Richards, J., 1999. Remote Sensing Digital Image Analysis, Springer.
29. Rosenberg, C., Hebert, M., Schneiderman, H., 2005. Semi-Supervised Self-Training of Object Detection Models. In: Seventh IEEE Workshops on Application of Computer Vision, 2005. WACV/MOTIONS '05, vol. 1, pp. 29-36.
30. Samat A., Du P., Liu S., Li J., Cheng L. (ELMs)-L-2: Ensemble Extreme Learning Machines for Hyperspectral Image Classification. IEEE J. Select. Topics Appl. Earth Observ. Remote Sens. 2014, 7(4):1060-1069.
31. Schölkopf, B., Smola, A.J., 2002. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press.
32. Shahshahani B.M., Landgrebe D.A. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Trans. Geosci. Remote Sens. 1994, 32(5):1087-1095.
33. Tan K., Du P.J. Hyperspectral remote sensing image classification based on support vector machine. J. Infrared Millimeter Waves 2008, 27(2):123-128.
34. Tan K., Li E., Du Q., Du P. An efficient semi-supervised classification approach for hyperspectral imagery. ISPRS J. Photogram. Remote Sens. 2014, 97(11):36-45.
35. 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, Cybernet., Part B: Cybernet. 2010, 40(5):1267-1279.
36. Tong S., Koller D. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2002, 2(1):45-66.
37. Tuia D., Ratle F., Pacifici F., Kanevski M.F., Emery W.J. Active learning methods for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 2009, 47(7):2218-2232.
38. Wang L., Hao S., Wang Y., Lin Y., Wang Q. Spectral information-based semisupervised classification algorithm for hyperspectral imagery. IEEE J. Select. Topics Appl. Earth Observ. Rem. Sens. 2014, 7(8):3577-3585.
39. Wilson T., Felt R. Hyperspectral remote sensing technology (HRST) program. Aerospace Conference, 1998 IEEE 1998, 193-200. IEEE.
40. Yao-hua, L., Zhong-ping, T., Ke, G., Mao-zhi, W., 2012. The Application research of hyperspectral remote sensing technology in tailing mine environment pollution supervise management. In: Second International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE), IEEE, 2012. pp. 1-4.
41. Yuan, P., Chen, Y., Jin, H., Huang, L., 2008. MSVM-kNN: Combining SVM and k-NN for multi-class text classification. In: IEEE International Workshop on Semantic Computing and Systems, 2008. WSCS'08. IEEE, pp. 133-140.
42. Zha Z.-J., Mei T., Wang J., Wang Z., Hua X.-S. Graph-based semi-supervised learning with multiple labels. J. Vis. Commun. Image Represent. 2009, 20(2):97-103.
43. Zhong Y., Zhang L. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2012, 50(3):894-909.
44. Zhong Y., Zhang L., Huang B., Li P. An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2006, 44(2):420-431.
45. Zhu, X., Lafferty, J., 2005. Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning, In: Proceedings of the 22nd International Conference on Machine learning, ACM, pp. 1052-1059.