An unsupervised band selection algorithm for hyperspectral imagery based on maximal information

被引：0

作者：

Liu, Xue-Song ^{[1
]}

Ge, Liang ^{[1
]}

Wang, Bin ^{[1
,2
]}

Zhang, Li-Ming ^{[1
]}

机构：

[1] Department of Electronic Engineering, Fudan University

[2] The Key Laboratory of Wave Scattering and Remote Sensing Information, Fudan University

来源：

Hongwai Yu Haomibo Xuebao/Journal of Infrared and Millimeter Waves | 2012年 / 31卷 / 02期

关键词：

Band selection; Classification; Hyperspectral imagery; Information amount; K-L divergence;

D O I：

10.3724/sp.j.1010.2012.00166

中图分类号：

学科分类号：

摘要：

An unsupervised band selection algorithm for hyperspectral imagery based on maximal information is proposed in this paper. The objective of the method is to preserve the maximal information from original data in the selected bands. The bands with less information are removed one by one from the original data. K-L divergence is used to quantify the information amount and its distribution over all the dataset is considered to judge the specific band which needs to be removed. Compared with traditional methods, the proposed approach has an explicit physical meaning and its computational process is very simple. It is an unsupervised method and can perform automatically.

引用

页码：166 / 170+176

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