Hyperspectral anomalous change detection in the presence of non stationary atmospheric/illumination conditions

Anomalous change detection (ACD) in HyperSpectral Images (HSIs) is a challenging task aimed at detecting a set of pixels that have undergone a relevant change with respect to a previous acquisition. Two main problems arise in ACD: a) the two multi-temporal HSIs are not radiometrically comparable because they are usually collected under different atmospheric/illumination conditions; b) it is difficult to obtain a perfect alignment of the two images especially when the sensor is mounted on airborne platforms. Several algorithms were proposed in the past to deal with the problem related to the radiometrical differences in the multi-temporal image pair. Most of them assumes the spatial stationarity of the atmospheric/illumination conditions in each of the two images and does not account for the possible presence of shadows. We propose a new ACD scheme that is robust to space-variant acquisition conditions. The ACD task is performed on two feature images extracted individually from each HSI. The feature images are selected to guarantee the robustness to the space-variant acquisition conditions in both the HSIs. They are the decision statistics provided by the RX anomaly detection algorithm applied individually to each HSI. In the paper, the advantages and the limits of the new ACD strategy are discussed and the results obtained by comparing the performance of such a strategy with that of a state-of-The -art ACD algorithm on real data are presented.