An efficient semi-supervised classification approach for hyperspectral imagery

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 97, Issue , 2014, Pages 36-45

  Tan, Kun ^a   Li, Erzhu ^a   Du, Qian ^b   Du, Peijun ^c  

^a CHINA UNIVERSITY OF MINING AND TECHNOLOGY   (China)

^b MISSISSIPPI STATE UNIVERSITY   (United States)

^c NANJING UNIVERSITY   (China)

Author keywords

Classification; Hyperspectral; Segmentation; Semi supervised learning; Spectral spatial feature; SVM

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE SEGMENTATION; INDEPENDENT COMPONENT ANALYSIS; SPECTROSCOPY; SUPERVISED LEARNING; SUPPORT VECTOR MACHINES;

HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL; HYPERSPECTRAL IMAGE CLASSIFICATION; SEGMENTATION ALGORITHMS; SEMI- SUPERVISED LEARNING; SEMI-SUPERVISED CLASSIFICATION; SPATIAL FEATURES; SVM;

IMAGE CLASSIFICATION;

ALGORITHM; IMAGE ANALYSIS; IMAGE CLASSIFICATION; PERFORMANCE ASSESSMENT; SPECTRAL ANALYSIS;

EID: 84907184537     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2014.08.003     Document Type: Article

References (41)

1. Bai J., Xiang S.M., Pan C.H. A graph-based classification method for hyperspectral images. IEEE Trans. Geosci. Remote Sens. 2013, 51(2):803-817.
2. 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.
3. Bioucas-Dias J.M., Nascimento J.M.P. Hyperspectral subspace identification. IEEE Trans. Geosci. Remote Sens. 2008, 46(8):2435-2445.
4. Bruzzone L., Chi M.M., Marconcini M. A novel transductive SVM for semisupervised classification of remote-sensing images. IEEE Trans. Geosci. Remote Sens. 2006, 44(11):3363-3373.
5. 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.
6. 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.
7. Camps-Valls G., Marsheva T.V.B., Zhou D.Y. Semi-supervised graph-based hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2007, 45(10):3044-3054.
8. Chapelle O., Sindhwani V., Keerthi S.S. Optimization techniques for semi-supervised support vector machines. J. Mach. Learn. Res. 2008, 9:203-233.
9. Comaniciu D., Meer P. Mean shift: a robust approach toward feature space analysis. Pattern Anal. Mach. Intell., IEEE Trans. 2002, 24(5):603-619.
10. Culp M., Michailidis G., Johnson K. On multi-view learning with additive models. Ann. Appl. Stat. 2009, 3(1):292-318.
11. Dopido I., Li J., Marpu P.R., Plaza A., Dias J.M.B., Benediktsson J.A. Semisupervised self-learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2013, 51(7):4032-4044.
12. Du B., Zhang L.P. Target detection based on a dynamic subspace. Pattern Recognit. 2014, 47(1):344-358.
13. Du P.J., Tan K., Xing X.S. Wavelet SVM in reproducing kernel hilbert space for hyperspectral remote sensing image classification. Opt. Commun. 2010, 283(24):4978-4984.
14. Du P.J., Xia J.S., Zhang W., Tan K., Liu Y., Liu S.C. Multiple classifier system for remote sensing image classification: a review. Sensors 2012, 12(4):4764-4792.
15. Du Q., Raksuntorn N., Younan N.H., King R.L. End-member extraction for hyperspectral image analysis. Appl. Opt. 2008, 47(28):F77-F84.
16. Du Q., Yang H. Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geosci. Remote Sens. Lett. 2008, 5(4):564-568.
17. Fauvel M., Benediktsson J.A., Chanussot J., Sveinsson J.R. Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Trans. Geosci. Remote Sens. 2008, 46(11):3804-3814.
18. Georgescu, B., Shimshoni, I., Meer, P., 2003. Mean shift based clustering in high dimensions: a texture classification example, Computer Vision, 2003. In: Proceedings, Ninth IEEE International Conference on. IEEE, pp. 456-463.
19. Gu Y.F., Feng K. Optimized Laplacian SVM with distance metric learning for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2013, 6(3):1109-1117.
20. Hughes G. On the mean accuracy of statistical pattern recognizers. IEEE Trans. Inf. Theory 1968, 14(1):55-63.
21. Jain A.K., Duin R.P.W., Mao J.C. Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(1):4-37.
22. Jia X.P., Kuo B.C., Crawford M.M. Feature mining for hyperspectral image classification. Proc. IEEE 2013, 101(3):676-697.
23. Krithara A., Amini M.R., Goutte C., Renders J.M. Learning aspect models with partially labeled data. Pattern Recognit. Lett. 2011, 32(2):297-304.
24. Li J., Bioucas-Dias J.M., Plaza A. Semisupervised hyperspectral image classification using soft sparse multinomial logistic regression. IEEE Geosci. Remote Sens. Lett. 2013, 10(2):318-322.
25. 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.
26. Manolakis D., Siracusa C., Shaw G. Hyperspectral subpixel target detection using the linear mixing model. IEEE Trans. Geosci. Remote Sens. 2001, 39(7):1392-1409.
27. Marconcini M., Camps-Valls G., Bruzzone L. A composite semisupervised SVM for classification of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 2009, 6(2):234-238.
28. Melgani F., Bruzzone L. Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans. Geosci. Remote Sens. 2004, 42(8):1778-1790.
29. Mountrakis G., Im J., Ogole C. Support vector machines in remote sensing: a review. ISPRS J. Photogramm. Remote Sens. 2011, 66(3):247-259.
30. Munoz-Mari J., Bovolo F., Gomez-Chova L., Bruzzone L., Camps-Valls G. Semisupervised one-class support vector machines for classification of remote sensing data. IEEE Trans. Geosci. Remote Sens. 2010, 48(8):3188-3197.
31. 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:S110-S122.
32. Ratle F., Camps-Valls G., Weston J. Semisupervised neural networks for efficient hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2010, 48(5):2271-2282.
33. Shi Q., Zhang L., Du B. Semi-supervised discriminative locally enhanced alignment for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 2013, 51(9):4800-4815.
34. Sun S.L., Shawe-Taylor J. Sparse semi-supervised learning using conjugate functions. J. Mach. Learn. Res. 2010, 11:2423-2455.
35. Tan K., Du P.J. Hyperspectral remote sensing image classification based on support vector machine. J. Infrared Millimeter Waves 2008, 27(2):123-128.
36. Tan K., Du P.J. Combined multi-kernel support vector machine and wavelet analysis for hyperspectral remote sensing image classification. Chin. Opt. Lett. 2011, 9(1):11003-11006.
37. Tan K., Li E., Du Q., Du P. Hyperspectral image classification using band selection and morphological profiles. Sel. Top. Appl. Earth Observations Remote Sens., IEEE J. 2013, 7(1):40-48.
38. van der Meer F.D., van der Werff H., van Ruitenbeek F.J., Hecker C.A., Bakker W.H., Noomen M.F., van der Meijde M., Carranza E.J.M., Smeth J., Woldai T. Multi-and hyperspectral geologic remote sensing: a review. Int. J. Appl. Earth Observation Geoinf. 2012, 14(1):112-128.
39. Villa A., Chanussot J., Benediktsson J.A., Jutten C., Dambreville R. Unsupervised methods for the classification of hyperspectral images with low spatial resolution. Pattern Recognit. 2013, 46(6):1556-1568.
40. Yang H., Du Q.A., Ma B. Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 2010, 7(4):875-879.
41. Zhong Y.F., Zhang L.P. An adaptive artificial immune network for supervised classification of multi-/hyperspectral remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 2012, 50(3):894-909.