Hyperspectral anomaly detection based on uniformly partitioned pixel

The objective of this paper is to develop an algorithm to detect anomaly in a hyperspectral image. The algorithm is based on a subspace model that is derived statistically. The anomaly detector is defined as the Mahalanobis distance of a residual from a pixel that is partitioned uniformly. The high correlation among adjacent components of the pixel is exploited by partitioning the pixel uniformly to improve anomaly detection. The residual is obtained by partialling out the main background from the pixel by predicting a linear combination of each partition of the pixel with a linear combination of the random variables representing the main background. Experimental results show that the anomaly detector outperforms conventional anomaly detectors.