12 May 2010 Segmentation adaptive RX: an algorithm for spectral anomaly detection in a variety of measured-radiance conditions
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Abstract
Hyperspectral anomaly detection algorithms are well developed however their ability to account for illumination conditions is limited only to mild variations. We propose an approach specifically designed to handle shadows and poorly illuminated regions present in otherwise well-illuminated imagery without making any assumptions about shaded backgrounds or object signature evolution. The algorithm, Segmentation Adaptive RX (SARX), relies on panchromatic segmentation of the data into dark and bright clusters based on the illumination level. Bright cluster detection employs standard subspace RX and dark cluster detection subspace is limited by only few higher variance spectral dimensions to reflect diminished signal-to-noise ratio in shadows. Anomaly detection near the geographical border between the clusters utilizes Stochastic Mixing Model. We demonstrate experimentally superior ability of SARX to detect anomalous objects in variety of illumination conditions.
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A. V. Kanaev, A. V. Kanaev, J. Murray-Krezan, J. Murray-Krezan, } "Segmentation adaptive RX: an algorithm for spectral anomaly detection in a variety of measured-radiance conditions", Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 769505 (12 May 2010); doi: 10.1117/12.850741; https://doi.org/10.1117/12.850741
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