[1]LI Shijin,CHANG Chun,YU Yufeng,et al.Multi-classifier combination-based hyperspectral band selection[J].CAAI Transactions on Intelligent Systems,2014,9(3):372-378.[doi:10.3969/j.issn.1673-4785.201404006]
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Multi-classifier combination-based hyperspectral band selection

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 9 Number of periods: 2014 3 Page number: 372-378 Column: 学术论文—智能系统 Public date: 2014-06-25
Title:
Multi-classifier combination-based hyperspectral band selection
Author(s):
LI Shijin;  CHANG Chun;  YU Yufeng;  WANG Yaming
College of Computer and Information, Hohai University, Nanjing 210098, China
Keywords:
hyperspectral remote sensing; pattern classification; band selection; multiple classifiers combination; error diversity; dimension reduction
CLC:
TP18
DOI:
10.3969/j.issn.1673-4785.201404006
Abstract:
Due to the multi-waveband and massive data characteristics of hyperspectral data, dimension reduction is becoming a distinct problem in regards to hyperspectral remote sensing research. A hyperspectral band selection algorithm has been proposed based on a multi-classifier combination. This algorithm obtains several groups of sub-optimal initial waveband subsets through a genetic algorithm, which has better optimizing ability and on this basis trains several base classifiers with these waveband subsets, and further selects several member classifiers from the initial classifier pool by using an improved classifier selection method that is based on same-fault measures, realizing the purpose of the wave band selection. And finally, the multi-classifier combination decision is made through dynamic classifier selection based on the analysis of local classification accuracy (DCS-LA). The experimental results regarding the Indian Pine benchmark data set show that this new method can select these bands with more discriminative information and obviously improve the accuracy of the classification process.
References:
[1] LANDGREBE D A. Signal theory methods in multispectral remote sensing[M]. John Wiley & Sons, 2005:5-12.
[2] SERPICO S B, BRUZZONE L. A new search algorithm for feature selection in hyperspectral remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7):1360-1367.
[3] BAZI Y, MELGANI F. Toward an optimal SVM classification system for hyperspectral remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11):3374-3385.
[4] SOTOCA J M, PLA F. Hyperspectral data selection from mutual information between image bands[M]. Berlin:Springer, 2006:853-861.
[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[J]. Pattern Recognition, 2008, 41(5):1653-1662.
[6] 戴宏亮, 戴道清.基于ETAFSVM的高光谱遥感图像自动波段选择和分类[J].计算机科学, 2009, 36(4):268-272.DAI Hongliang, DAI Daoqing. Automatic band selection and classification of hyperspectral remote sensing images based on ETAFSVM[J]. Computer Science, 2009, 36(4):268-272.
[7] 吴昊, 李士进, 林林, 等.多策略结合的高光谱图像波段选择新方法[J]. 计算机科学与探索, 2010, 4(5):464-472.WU Hao, LI Shijin, LIN Lin, et al. Multiple-strategy combination based approach to band selection for hyper-spectral image classification[J]. Journal of Frontiers of Computer Science and Technology, 2010, 4(5):464-472.
[8] YANG He, DU Qian, SU Hongjun, et al. An efficient method for supervised hyperspectral band selection[J]. IEEE Geosci Remote Sensing Lett, 2011, 8(1):138-142.
[9] 葛亮, 王斌, 张立明.基于波段聚类的高光谱图像波段选择[J].计算机辅助设计与图形学学报, 2012, 24(11):1447-1454.GE Liang, WANG Bing, ZHANG Liming. Band selection based on band clustering for hyperspectral imagery[J]. Journal of Computer-Aided Design & Computer Graphics, 2012, 24(11):1447-1454.
[10] 王立国, 魏芳洁.结合遗传算法和蚁群算法的高光谱图像波段选择[J].中国图象图形学报, 2013, 18(2):235-242.WANG Liguo, WEI Fangjie.Band selection for hyperspectral imagery based on combination of genetic algorithm and ant colony algorithm[J].Journal of Image and Graphics, 2013, 18(2):235-242.
[11] LI Shijin, ZHU Yuelong, WAN Dingsheng, et al.Spectral similarity-preserving hyperspectral band selection[J]. Remote Sensing Letters, 2013, 4(10):969-978.
[12] SERPICO S B, MOSER G. Extraction of spectral channels from hyperspectral images for classification purposes[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(2):484-495.
[13] ZHOU Z H. Ensemble Methods:Foundations and algorithms[M]. Boca Raton:CRC Press, 2012.
[14] KUNCHEVA L I. Switching between selection and fusion in combining classifiers:an experiment[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 2002, 32(2):146-156.
[15] KUNCHEVA L I, WHITAKER C J. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy[J]. Machine Learning, 2003, 51(2):181-207.
[16] WOODS K, KEGELMEYER JR W P, BOWYER K. Combination of multiple classifiers using local accuracy estimates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(4):405-410.
[17] ZHOU Z H, WU J X, TANG W. Ensembling neural networks:many could be better than all[J]. Artificial Intelligence, 2002, 137(1/2):239-263.
[18] 张春霞, 张讲社.选择性集成学习算法综述[J].计算机学报, 2011, 34(8):1399-1410.ZHANG Chunxia, ZHANG Jiangshe.A survey of selective ensemble learning algorithms[J]. Chinese Journal of Computers, 2011, 34(8):1399-1410.
[19] AKSELA M, LAAKSONEN J. Using diversity of errors for selecting members of a committee classifier[J]. Pattern Recognition, 2006, 39(4):608-623.
[20] BENEDIKTSSON J A, CHANUSSOT J, FAUVEL M. Multiple classifier systems in remote sensing:from basics to recent developments[C]//Proceedings of the 7th International Conference on Multiple Classifier Systems.:Berlin:Springer-Verlag, 2007:501-512.
[21] MARINA S, ROBERT P W.Combining feature subsets in feature selection[C]//Proceedings of the 5th International Conference on Multiple Classifier Systems. Berlin, 2005:165-175.
[22] XAVIER C, BJöRN W, JON A B, et al. Ensemble strategies for classifying hyperspectral remote sensing data[C]//Proceedings of the 9th International Conference on Multiple Classifier Systems, Berlin:Springer-Verlag, 2009:62-71.
[23] DU Peijun, ZHANG Wei, SUN Hao. Multiple classifier combination for hyperspectral remote sensing image classification[C]//Proceedings of the 9th International Conference on Multiple Classifier Systems, Berlin, 2009:52-61.
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