Application of context-based classifier to hyperspectral imagery for mine detection

Jeremy Bolton; Paul Gader

doi:10.1117/12.782351

29 April 2008 Application of context-based classifier to hyperspectral imagery for mine detection

Jeremy Bolton, Paul Gader

Proceedings Volume 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII; 695319 (2008) https://doi.org/10.1117/12.782351
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Jeremy Bolton and Paul Gader "Application of context-based classifier to hyperspectral imagery for mine detection", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695319 (29 April 2008); https://doi.org/10.1117/12.782351

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