PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Band Selection for Hyperspectral Image Classification using Mutual Information
Baofeng Guo, Steve Gunn, Bob Damper and James Nelson
IEEE Geoscience and Remote Sensing Letters Volume 3, Number 4, pp. 522-526, 2006.

Abstract

Spectral band selection is a fundamental problemin hyperspectral data processing. In this paper, a new band-selection method based on mutual information (MI) is proposed.MI measures the statistical dependence between two randomvariables and can therefore be used to evaluate the relativeutility of each band to classification. A new strategy is describedto estimate the mutual information using a priori knowledgeof the scene, reducing reliance on a ‘ground truth’ referencemap, by retaining bands with high associated MI values (subjectto certain so-called ‘complementary’ conditions). Simulations ofclassification performance on 16 classes of vegetation from theAVIRIS 92AV3C dataset show the effectiveness of the method,which outperforms an MI-based method using the associatedreference map, an entropy-based method and a correlation-basedmethod. It is also competitive with the steepest ascent (SA)algorithm at much lower computational cost.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2922
Deposited By:Steve Gunn
Deposited On:23 November 2006