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Bayesian context-dependent learning for anomaly classification in hyperspectral imagery

Publication ,  Journal Article
Ratto, CR; Jr, KDM; Collins, LM; Torrione, PA
Published in: IEEE Transactions on Geoscience and Remote Sensing
January 1, 2014

Many remote sensing applications involve the classification of anomalous responses as either objects of interest or clutter. This paper addresses the problem of anomaly classification in hyperspectral imagery (HSI) and focuses on robustly detecting disturbed earth in the long-wave infrared (LWIR) spectrum. Although disturbed earth yields a distinct LWIR signature that distinguishes it from the background, its distribution relative to clutter may vary over different environmental contexts. In this paper, a generic Bayesian framework is proposed for training context-dependent classification rules from wide-area airborne LWIR imagery. The proposed framework combines sparse classification models with either supervised or discriminative context identification to pool information across contexts and improve classification overall. Experiments are performed with data from a LWIR landmine detection system. Contexts are learned from endmember abundances extracted from the background near each detected anomaly. Classification performance is compared with single-classifier approaches using the same information as well as other baseline anomaly detectors from the literature. Results indicate that utilizing context for classifying anomalies in HSI could lead to more robust performance over varying terrain. © 2013 IEEE.

Duke Scholars

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

January 1, 2014

Volume

52

Issue

4

Start / End Page

1969 / 1981

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics
 

Citation

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MLA
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Ratto, C. R., Jr, K. D. M., Collins, L. M., & Torrione, P. A. (2014). Bayesian context-dependent learning for anomaly classification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(4), 1969–1981. https://doi.org/10.1109/TGRS.2013.2257175
Ratto, C. R., K. D. M. Jr, L. M. Collins, and P. A. Torrione. “Bayesian context-dependent learning for anomaly classification in hyperspectral imagery.” IEEE Transactions on Geoscience and Remote Sensing 52, no. 4 (January 1, 2014): 1969–81. https://doi.org/10.1109/TGRS.2013.2257175.
Ratto CR, Jr KDM, Collins LM, Torrione PA. Bayesian context-dependent learning for anomaly classification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2014 Jan 1;52(4):1969–81.
Ratto, C. R., et al. “Bayesian context-dependent learning for anomaly classification in hyperspectral imagery.” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 4, Jan. 2014, pp. 1969–81. Scopus, doi:10.1109/TGRS.2013.2257175.
Ratto CR, Jr KDM, Collins LM, Torrione PA. Bayesian context-dependent learning for anomaly classification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing. 2014 Jan 1;52(4):1969–1981.

Published In

IEEE Transactions on Geoscience and Remote Sensing

DOI

ISSN

0196-2892

Publication Date

January 1, 2014

Volume

52

Issue

4

Start / End Page

1969 / 1981

Related Subject Headings

  • Geological & Geomatics Engineering
  • 40 Engineering
  • 37 Earth sciences
  • 0909 Geomatic Engineering
  • 0906 Electrical and Electronic Engineering
  • 0404 Geophysics