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Proceedings Paper

Application of context-based classifier to hyperspectral imagery for mine detection
Author(s): Jeremy Bolton; Paul Gader
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Paper Abstract

In remotely sensed hyperspectral imagery, many samples are collected on a given flight and many variable factors contribute to the distribution of samples. Measurements made at different flight times over the same swath may result in different spectral responses due to various environmental conditions and sensor calibration. Many classification methods attempt to classify a sample using labeled datasets or a priori information about the samples. We present a possibilistic context-based approach for class estimation within a random set model. This approach includes novel formulations for model parameters with an intuitive base in probability and measure theory. This approach implicitly retains contextually correlated information in the data and uses it to estimate class labels in the presence of unknown factors-hidden contexts. This new method is applied to AHI (hyperspectral) imagery for the purposes of landmine detection. The results are compared to conventional methods and analyzed.

Paper Details

Date Published: 29 April 2008
PDF: 8 pages
Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695319 (29 April 2008); doi: 10.1117/12.782351
Show Author Affiliations
Jeremy Bolton, Univ. of Florida (United States)
Paul Gader, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 6953:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII
Russell S. Harmon; John H. Holloway; J. Thomas Broach, Editor(s)

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