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PCA与移动窗小波变换的高光谱决策融合分类

DOI: 10.11834/jig.20150114

Keywords: 高光谱分类,主成分分析,小波变换,决策融合

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Abstract:

目的高光谱数据具有较高的谱间分辨率和相关性,给分类处理带来了一定的困难。为了提高分类精度,提出一种结合PCA与移动窗小波变换的高光谱决策融合分类算法。方法首先,利用相关系数矩阵对原始高光谱数据进行波段分组;然后,利用主成分分析对每组数据进行谱间降维;再根据提出的移动窗小波变换法进行空间特征提取;最后,采用线性意见池(LOP)决策融合规则对多分类器的分类结果进行融合。结果采用两组来自不同传感器的数据进行实验,所提算法的分类精度和Kappa系数均高于已有的5种分类算法。与SVM-RBF算法相比,本文算法的分类精度高出了8%左右。结论实验结果表明,本文算法充分挖掘了高光谱图像的谱间-空间信息,能有效提高分类正确率,在小样本情况下和噪声环境中也具有良好的分类性能。

References

[1]  Duda R O, Hart P E, Stork D G. Pattern Classification[M]. 2nd ed. New York: John Wiley & Sons, Inc., 2001.
[2]  Berge A, Solberg A H S. Structured Gaussian components for hyperspectral imgae classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(11): 3386-3396. [DOI: 10.1109/TGRS.2006.880626]
[3]  Zhao C H, Zhang Y, Wang Y L. Relevant vector machine classification of hyperspectral image based on wavelet kernel principal component analysis[J]. Journal of Electronics & Information Technology, 2012, 34(8): 1905-1910. [赵春晖, 张?, 王玉磊. 基于小波核主成分分析的相关向量机高光谱图像分类[J]. 电子与信息学报, 2012, 34(8): 1905-1910.][DOI:10.3724/39.J.1146.2011.01282]
[4]  Zhang J, Sun J X, Ruan G S, et al. Segmented 2DPCA algorithm for band selection of hyperspectral image[J]. Journal of Image and Graphics, 2014, 19(2): 328-332. [DOI: 10.11834/jig.20140220] [张靖,孙俊喜,阮光诗,等. 分段2维主成分分析的超光谱图像波段选择[J]. 中国图象图形学报,2014, 19(2): 328-332.[DOI:10.11834/jig.20140220]
[5]  Wang J, Chang C. Application of independent component analysis in endmember extraction and abundance quantification for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2601-2616. [DOI: 10.1109/TGRS.2006.874135]
[6]  Zhang Y, Zhou G, Zhao Q, et al. Spatial-temporal discriminant analysis for ERP-based brain-computer interface[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2013, 21(2): 233-243. [DOI: 10.1109/TNSRE.2013. 2243471]
[7]  Bruce L M, Koger C H, Jiang L. Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(10): 2331-2338. [DOI: 10. 1109/TGRS.2002. 804721]
[8]  Rajadell O, Garcia-Sevilla P, Pla F. Spectral-spatial pixel characterization using gabor filters for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4): 860-864. [DOI: 10.1109/LGRS.2012.2226426]
[9]  Li W, Prasad S, Fowler J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(4): 1185-1198. [DOI: 10.1109/TGRS.2011. 2165957]
[10]  Ye Z, Prasad S, Li W, et al. Locality-preserving discriminant analysis and Gaussian mixture models for spectral-spatial classification of Hyperspectral imagery[C]//Proceedings of the 4th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Shanghai, China:IEEE,2012:1-4. [DOI: 10.1109/WHISPERS.2012.6874299]
[11]  Li J, Bioucas-Dias J M, Plaza A. Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 809-823. [DOI: 10.1109/TGRS. 2011.2162649]
[12]  Kuo B, Ho H, Li C, et al. A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 7(1): 317-326. [DOI: 10.1109/JSTARS.2013. 2262926]
[13]  Du Q, Zhu W, Yang H, et al. Segmented principal component analysis for parallel compression of hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4): 713-717. [DOI: 10.1109/LGRS. 2009.2024175]
[14]  Nian Y J, Wang Z, Wan J W, et al. Compression technique for hyperspectral imagery oriented anomaly detection[J]. Journal of National University of Defense Technology, 2009, 31(3): 48-52. [粘永健, 王展, 万建伟, 等. 面向异常检测的高光谱图像压缩技术[J]. 国防科技大学学报, 2009, 31(3): 48-52.]
[15]  Prasad S, Bruce L M. Decision fusion with confidence-based weight assignment for hyperspectral target recognition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(5): 1448-1456. [DOI: 101109/TGRS. 2008. 916207

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