一种改进的K2DPCA高光谱遥感图像降维方法Hyperspectral remote sensing image dimensional reduction based on improved Kernel Two-dimensional Principal Component Analysis
白杨,赵银娣,韩天庆
摘要(Abstract)：
本文提出了一种改进的核二维主成分分析(K2DPCA)高光谱遥感图像降维方法,该方法通过标准核二维主成分分析消除了遥感图像各波段列间的相关性,利用列二维主成分分析在核二维主成分的行方向上进一步去除相关性,实现了遥感图像在空间维上的双向降维,并得到各波段的主成分,重建原始图像。采用AVIRIS和HyMap两种高光谱遥感图像进行试验,结果表明该方法在保证重构图像质量的同时,能够有效提高图像压缩比,在遥感图像降维中具有普适性。
关键词(KeyWords)： 二维主成分分析;核二维主成分分析;信息保持率;图像降维;图像重构
基金项目(Foundation): 国家自然科学基金(40901221,41001312);; 中国博士后科学基金(20090450182);; 江苏省普通高校研究生科研创新计划项目(CXLX12_0955);; 江苏高校优势学科建设工程项目(SZBF2011-6-B35)
作者(Author): 白杨,赵银娣,韩天庆
DOI: 10.16251/j.cnki.1009-2307.2014.07.035
参考文献(References)：
- [1]王晋年,张兵,刘建贵,等.以地物识别和分类为目标的高光谱数据挖掘[J].中国图象图形学报,1999,4A(11):957-964.
- [2]Kaarna A,Zemcik P,Klviinen H,et al.Compression of multispectral remote sensing images using clustering and spectral reduction[J]//IEEE Transactions on Geoscience and Remote Sensing,2000,38(2):1073-1082.
- [3]Scholkopf B,Smola A,Muller K R.Nonlinear component analysis as a Kernel eigenvalue problem[J].Neural Computation,1998,10(5):1299-1319.
- [4]吴一全,吴超.结合NSCT和KPCA的高光谱遥感图像去噪[J].遥感学报,2012,16(3):533-544.
- [5]Zhang Y W.Enhanced statistical analysis of nonlinear processes using KPCA,KICA and SVM[J].Chemical Engineering Science,2009,64(5):801-811.
- [6]Yang J,Zhang D,Frangi A F,Yang J Y.Two dimensional PCA:a new approach to appearance-based face representation and recognition[J]//IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(1):131-137.
- [7]Zhang D Q,Zhou Z H.(2D)2PCA:Two-directional two-dimensional PCA for efficient face representation and recognition[J].Neurocomputing,2005,69(1-3):224-231.
- [8]Yang J,Xu Y,Yang J Y.Bi-2DPCA:A fast face coding method for recognition[J].Pattern Recognition Recent Advances,2010(2):313-340.
- [9]Wahyuningrum R T,Damayanti F.Efficient kernelbased two-dimensional principal component analysis for smile stages recognition[J].TELKOMNIKA,2012,10(1):113-118.
- [10]Sun N,Wang H X,Ji Z H.An efficient algorithm for Kernel two-dimensional principal component analysis[J].Neural Comput&Applic,2008,17(1):59-64.
- [11]钱进,等.基于拉普拉斯特征映射高光谱遥感影像降维及其分类[J].遥感信息,2012,27(5).
- [12]王华忠,俞金寿.核函数方法及其模型选择[J].江南大学学报(自然科学版),2006,5(4):500-504.