ICA AND KERNEL ICA FOR CHANGE DETECTION IN MULTISPECTRAL REMOTE SENSING IMAGES

被引：32

作者：

Marchesi, Silvia ^{[1
]}

Bruzzone, Lorenzo ^{[1
]}

机构：

[1] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Povo, Trento, Italy

来源：

2009 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-5 | 2009年

关键词：

Change detection; principal component analysis; independent component analysis; kernel independent component analysis; multispectral images; remote sensing; INDEPENDENT COMPONENT ANALYSIS; UNSUPERVISED CHANGE DETECTION;

D O I：

10.1109/IGARSS.2009.5418265

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

In this paper Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Kernel Independent Component Analysis (KICA) are studied and compared in the framework of unsupervised change detection in multitemporal remote sensing images Different architectures for using the above-mentioned techniques in change detection are investigated, and their capability to discriminate true changes from the different sources of noise analyzed. Experimental results obtained on a pair of very high geometrical resolution Quickbird images point out the main properties of the different methods when applied to change detection.

引用

页码：1231 / 1234

页数：4

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