A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification

TitleA comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification
Publication TypeConference Paper
Year of Publication2011
AuthorsCR Ratto, KD Morton, LM Collins, and PA Torrione
Conference NameWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Date Published12/2011
Abstract

Context-dependent learning algorithms have shown improved performance for anomaly classification in hyperspectral imagery (HSI) collected over varying environmental conditions. Past techniques have relied on statistically-motivated decomposition, such as principal components analysis (PCA), to extract contextual information from the background data. Alternatively, physics-based endmember approaches could also be used to extract contextual features. In this work, context-dependent classifiers using both types of contextual features were applied to a landmine detection problem in HSI. Context-dependent learning showed improvements in classification performance over conventional learning, and the endmember-based and PCA-based context modeling techniques yielded similar overall model behavior which is investigated. © 2011 IEEE.

DOI10.1109/WHISPERS.2011.6080927