Improving Hyperspectral Anomaly Detection With a Simple Weighting Strategy

Numerous hyperspectral anomaly detection (AD) methods suffer from complex background compositions and subpixel objects due to their inadequate Gaussian-distributed representations for nonhomogeneous backgrounds or their low discrimination between subpixel anomalies and the background. To alleviate these issues, a novel hyperspectral AD weighting strategy based on tensor decomposition and cluster weighting is proposed in this letter. Equipped with this simple but effective strategy as a postprocess, the detection performances of generic AD methods can he significantly boosted. In this strategy, Tucker decomposition is adopted to remove the major background information. A parameter-adaptive k-means clustering method is then applied on the decomposed anomaly/noise data cube to assemble homogeneous regions. After segmenting the clustering result into a number of nonoverlapping eight-connected domains, corresponding weights are assigned to large domains according to an improved Gaussian weight function. Finally, the resulting weight matrix is multiplied by the results of the detectors to achieve a performance boost. Experiments on two hyperspectral data sets validate the effectiveness of the proposed strategy.