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Boosted band ratio feature selection for hyperspectral image classification

Date

Authors

Fu, Zhouyu

Caelli, Terry

Liu, Nianjun

Robles-Kelly, Antonio

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

Band ratios have many useful applications in hyperspectral image analysis. While optimal ratios have been chosen empirically in previous research, we propose a principled algorithm for the automatic selection of ratios directly from data. First, a robust

Keywords

Keywords: Algorithms; Automation; Classification (of information); Convergence of numerical methods; Image analysis; Large scale systems; Automatic selection; Hyperspectral image classification; Individual ratio features; Kullback-Leibler divergence (KLD); Feature

URI

http://hdl.handle.net/1885/32932

Collections

ANU Research Publications

Source

Proceedings of the 18th International Conference on Pattern Recognition

Type

Conference paper

DOI

10.1109/ICPR.2006.334

Restricted until

2037-12-31

Downloads

File

Description

01_Fu_Boosted_band_ratio_feature_2006.pdf (276.3 KB)

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