Boosted band ratio feature selection for hyperspectral image classification
Date
2006
Authors
Fu, Zhouyu
Caelli, Terry
Liu, Nianjun
Robles-Kelly, Antonio
Journal Title
Journal ISSN
Volume Title
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
Description
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
Citation
Collections
Source
Proceedings of the 18th International Conference on Pattern Recognition
Type
Conference paper
Book Title
Entity type
Access Statement
License Rights
Restricted until
2037-12-31
Downloads
File
Description