A FAST ITERATIVE KERNEL PCA FEATURE EXTRACTION FOR HYPERSPECTRAL IMAGES

被引：15

作者：

Liao, Wenzhi ^{[1
,2
]}

Pizurica, Aleksandra ^{[1
]}

Philips, Wilfried ^{[1
]}

Pi, Youguo ^{[2
]}

机构：

[1] Univ Ghent, TELIN IPI IBBT, Sint Pietersnieuwstr 41, B-9000 Ghent, Belgium

[2] South China Univ Technol, Sch Automat, Guangzhou 510640, Peoples R China

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING | 2010年

关键词：

Feature extraction; hyperspectral images; incremental principal component analysis; kernel vesion; COMPONENT ANALYSIS;

D O I：

10.1109/ICIP.2010.5651670

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A fast iterative Kernel Principal Component Analysis (KPCA) is proposed to extract features from hyperspectral images. The proposed method is a kernel version of the Candid Covariance-Free Incremental Principal Component Analysis, which solves the eigenvectors through iteration. Without performing eigen decomposition on Gram matrix, our method can reduce the space complexity and time complexity greatly. Experimental results were validated in comparison with the standard KPCA and linear version methods.

引用

页码：1317 / 1320

页数：4

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