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

Publication ,  Conference
Ratto, CR; Morton, KD; Collins, LM; Torrione, PA
Published in: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
December 28, 2011

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.

Duke Scholars

Published In

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

DOI

ISSN

2158-6276

Publication Date

December 28, 2011
 

Citation

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Ratto, C. R., Morton, K. D., Collins, L. M., & Torrione, P. A. (2011). A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification. In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. https://doi.org/10.1109/WHISPERS.2011.6080927
Ratto, C. R., K. D. Morton, L. M. Collins, and P. A. Torrione. “A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification.” In Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2011. https://doi.org/10.1109/WHISPERS.2011.6080927.
Ratto CR, Morton KD, Collins LM, Torrione PA. A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification. In: Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. 2011.
Ratto, C. R., et al. “A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification.” Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, 2011. Scopus, doi:10.1109/WHISPERS.2011.6080927.
Ratto CR, Morton KD, Collins LM, Torrione PA. A comparison of principal components and endmember-based contextual learning for hyperspectral anomaly classification. Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. 2011.

Published In

Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

DOI

ISSN

2158-6276

Publication Date

December 28, 2011