首页 > 论文 > 红外技术 > 39卷 > 12期(pp:1107-1113)

采用分段行-列核2DPCA的高光谱图像降维

Dimensionality Reduction for Hyperspectral Image Using a Segmented Row-column Kernel Two-dimensional Principal Component Analysis Method

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

二维主成分方法计算时间少,降维效果好,被成功应用到高光谱图像降维中。基于二维主成分方法,为挖掘高光谱图像的非线性信息,实现了分段行-列核2DPCA 方法的降维,并对比分析了行-列2DPCA 方法、分段行-列2DPCA 方法和行-列核2DPCA 方法的降维效果。利用相关性将高光谱图像划分为5 个子空间,通过转换数据结构来实现行和列的核2DPCA 变换,最后将行和列结果进行融合得到降维结果。降维结果表明,在较高信息保持率情况下,分段行-列核2DPCA 方法具有最高的图像清晰度和边缘强度。不同地物像元像素折线图表明,分段行-列核2DPCA 方法能更好地区分不同地物,可以很好地应用于地物分类和目标识别。

Abstract

Two-dimensional principal component analysis (2DPCA) is successfully applied to a hyperspectral image, and is less time-consuming with better dimensionality reduction performance. Based on the two-dimensional principal component method, the segmented row-column kernel 2DPCA algorithm is realized to excavate non-linear information. The dimension reduction effect of the row-column 2DPCA method, the segmented row-column 2DPCA method, and the row-column kernel 2DPCA method are compared and analyzed. The hyperspectral image is divided into five subspaces by correlation, and the kernel 2DPCA of rows and columns is realized by transforming the data structure. Finally, the row and column results are merged to obtain dimensionality reduction results. The reduced dimension results show that the segmented row-column kernel 2DPCA method has the highest figure definition and edge intensity at a higher information retention rate. The pixel line graph indicates that the proposed method can distinguish the different features better, and is well suited to classification and target recognition.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP751.1

所属栏目:图像处理与仿真

基金项目:吉林省教育厅“十二五”科研项目(2015448);吉林省科技发展计划资助项目(20140101213JC)。

收稿日期:2017-02-23

修改稿日期:2017-04-05

网络出版日期:--

作者单位    点击查看

向英杰:空军航空大学,吉林 长春 130000
杨 桄:空军航空大学,吉林 长春 130000
张俭峰:空军航空大学,吉林 长春 130000
王 琪:空军航空大学,吉林 长春 130000

联系人作者:向英杰(xyjandsy@163.com)

备注:向英杰(1993-),男,硕士研究生,主要研究方向为高光谱图像解译。

【1】张兵.高光谱图像处理与信息提取前沿[J].遥感学报,2016,20(5):1061-1090.
ZHANG Bing. The advances of hyperspectral image processing and information extraction[J]. Journal of Remote Sensing, 2016, 20(5): 1061-1090.

【2】José M, Antonio Plaza, Gustavo Camps, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience And Remote Sensing Magazine, 2013, 1(2): 6-36.

【3】YANG J, ZHANG D. 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.

【4】ZHANG D Q, ZHOU Z H. (2D)2PCA:Two-directional two-dimensional PCA for efficient face representation and recognition[J]. Ncuro Computing, 2005, 69(1-3): 224-231.

【5】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.

【6】Wahyuningrum R T, Damayanti E. Efficient kernel-based two -dimensional principal component analysis for smile stages recognition[J]. TELKOMNIKA, 2012, 10(1): 113-118.

【7】SUN N, WANG H X, JI Z H, et al. An efficient algorithm for Kernel two-dimensional principal component analysis[J]. Neural Comput. & Applic., 2008, 17: 59-64.

【8】赵春晖,宋晓玥.基于二维主成分分析的高光谱遥感图像降维[J].黑龙江大学自然科学学报,2009,26(5):684-688.
ZHAO C H, SONG X Y. Hyperspectral remote sensing image dimension reduction based on two-dimensional principal component analysis[J]. Journal of Natural Science of Heilongjiang University, 2009, 26(5): 684-688.

【9】张婧, 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]. 中国图形图象学报, 2014, 19(2): 328-332. 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.

【10】杨明,张鹏强,余旭初,等.采用二维主成分分析的高光谱影像分类[J].测绘科学,2015,40(6):139-145.
YANG M, ZHANG P Q, YU X C, et al. Hyperspectral image classification using two- dimensional principal component analysis[J]. Science of Surveying and Mapping, 2015, 40(6): 139-145.

【11】白杨,赵银娣,韩天庆.一种改进的K2DPCA高光谱遥感图像降维方法[J].测绘科学,2014,39(7):126-139.
BAI Y, ZHAO Y T, HAN T Q. An improved K2DPCA dimension reduction method for hyperspectral remote sensing images[J]. Science of Surveying and Mapping, 2014, 39(7): 126-139.

【12】赵春晖,胡春梅,石红.采用选择性分段PCA算法的高光谱图像异常检测[J].哈尔滨工程大学学报,2011,32(1):109-113.
ZHAO C H, HU C M, SHI H. Anomaly detection for a hyperspectral image by using a selective section principal component analysis algorithm[J]. Journal of Harbin Engineering University, 2011, 32(1): 109-113.

引用该论文

XIANG Yingjie,YANG Guang,ZHANG Jianfeng,WANG Qi. Dimensionality Reduction for Hyperspectral Image Using a Segmented Row-column Kernel Two-dimensional Principal Component Analysis Method[J]. Infrared Technology, 2017, 39(12): 1107-1113

向英杰,杨 桄,张俭峰,王 琪. 采用分段行-列核2DPCA的高光谱图像降维[J]. 红外技术, 2017, 39(12): 1107-1113

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF