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Improving anomaly detection with Multinormal Mixture Models in shadow

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4 Author(s)
Haavardsholm, T. ; Norwegian Defence Res. Establ. (FFI), Kjeller, Norway ; Kavara, A. ; Kasen, I. ; Skauli, T.

Hyperspectral images are well suited for automatic target detection, but detection performance in shadow is often degraded due to effects such as low signal-to-noise ratio, high dynamic range and spectral distortions. This paper focuses on improving target detection performance for a specific anomaly detector based on a statistical Multinormal Mixture Model (MMM) that is trained on the entire image to produce a global model of the background. It is demonstrated that a simple square root transformation and a hyperspheric transformation may be applied to the radiance image to enhance detection performance. A balancing strategy for the training of the model with respect to light level is shown to be a further improvement.

Published in:

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Date of Conference:

22-27 July 2012

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