A hybrid evolutionary approach to band selection for hyperspectral image classification

Lecture Notes in Electrical Engineering

Volumn 67 LNEE, Issue , 2010, Pages 329-336

  Wu, Hao ^a   Zhu, Jiali ^a   Li, Shijin ^a   Wan, Dingsheng ^a   Lin, Lin ^b  

^a HOHAI UNIVERSITY   (China)

^b Institute of Water Resources and Hydropower Research   (China)

Author keywords

Band Selection; Branch and Bound Algorithm; Conditional Mutual Information; Genetic Algorithm; Hyperspectral Remote Sensing; Support Vector Machine

Indexed keywords

BAND SELECTION; BENCHMARK DATA; BRANCH-AND-BOUND ALGORITHMS; CONDITIONAL MUTUAL INFORMATION; EVOLUTIONARY APPROACH; HYPERSPECTRAL IMAGE CLASSIFICATION; HYPERSPECTRAL REMOTE SENSING; IMAGING TECHNOLOGY;

BRANCH AND BOUND METHOD; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; LINEAR PROGRAMMING; NEURAL NETWORKS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

SPECTROSCOPY;

EID: 77957988574     PISSN: 18761100     EISSN: 18761119     Source Type: Book Series    
DOI: 10.1007/978-3-642-12990-2_37     Document Type: Conference Paper

References (18)

1. Landgrebe, D.A.: Signal theory methods in multispectral remote sensing. Wiley, Hoboken (2003)
2. Liu, C., Zhao, C., Zhang, Y.: A new method of hyperspectral remote sensing image dimensional reduction. Journal of Image and Graphics 10, 218-224 (2005)
3. Serpico, S.B., Bruzzone, L.: A new search algorithm for feature selection in hyperspectral remote sensing images. IEEE Trans. on Geoscience and Remote Sensing 39, 1360-1367 (2001)
4. Serpico, S.B., Moser, G.: Extraction of spectral channels from hyperspectral images for classification purposes. IEEE Trans. on Geoscience and Remote Sensing 45, 484-495 (2007)
5. Guo, B., Damper, R.I., Gunn, S.R., et al.: A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition 41, 1653-1662 (2008)
6. Huang, R., He, M.: Band selection based on feature weighting for classification of hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing Letters 2, 156-159 (2005)
7. Wang, L., Gu, Y., Zhang, Y.: Band selection method based on combination of support vector machines and subspatial partition. Systems Engineering and Electronics 27, 974-977 (2005)
8. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 1226-1238 (2005)
9. Sotoca, J.M., Pla, F.: Hyperspectral data selection from mutual information between image bands. In: Yeung, D.-Y., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds.) SSPR 2006 and SPR 2006. LNCS, vol. 4109, pp. 853-861. Springer, Heidelberg (2006)
10. Kwak, N., Choi, C.-H.: Input feature selection for classification problems. IEEE Trans. on Neural Networks 13, 143-159 (2002)
11. Novovicova, J., Somol, P., Haindl, M., Pudil, P.: Conditional mutual information based feature selection for classification task. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 417-426. Springer, Heidelberg (2007)
12. Oh, I.-S., Lee, J.-S., Moon, B.-R.: Hybrid genetic algorithms for feature selection. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 1424-1437 (2004)
13. Chang, C., Lin, C.: LIBSVM: a library for support vector machines (2009), Software available at: http://www.csie.ntu.edu.tw/~cjlin/libsvm
14. Nakariyakul, S., Casasent, D.P.: Adaptive branch and bound algorithm for selecting optimal features. Pattern Recognition Letters 28, 1415-1427 (2007)
15. Zuo, L., Zheng, J., Wang, F., et al.: A genetic algorithm based wrapper feature selection method for classification of hyper spectral data using support vector machine. Geographical Research 27, 493-501 (2008)
16. Benediktsson, J.A., Palmason, J.A., Sveinsson, J.R.: Classification of hyperspectral data from urban areas based on extended morphological profiles. IEEE Trans. on Geoscience and Remote Sensing 43, 480-491 (2005)
17. Dundar, M.M., Landgrebe, D.A.: Toward an optimal supervised classifier for the analysis of hyperspectral data. IEEE Trans. on Geoscience and Remote Sensing 42, 271-277 (2004)
18. Kuo, B.C., Chang, K.Y.: Feature extractions for small sample size classification problem. IEEE Trans. on Geoscience and Remote Sensing 45, 756-764 (2007)