Hyperspectral anomaly detection based on the bilateral filter

Abstract

Bilateral filter outputs a weighted average of the neighboring information at a pixel. It can smooth the image and remove anomalous points while preserving edges. Inspired by this idea, this paper proposes a hyperspectral anomaly detection algorithm based on the bilateral filter. A pixel that spectrally differs from the background is regarded as anomalous. If every pixel in a hyperspectral image is represented by its surrounding background pixels, a relatively clean hyperspectral background image without anomalies can be obtained. We improve the weights that are imposed on the neighboring pixels in representation. First, spatial distance weight is selected to make full use of the space-varying information. Second, spectral Euclidean distance weight is used to express the spectral similarity. To reduce the negative impact that is caused by anomalies existing in neighboring background pixels, dual window is utilized to act on above two weights. Finally, anomalous information of hyperspectral image can be derived after making the subtraction between the original hyperspectral image and the derived hyperspectral background image. Extensive experiments on three hyperspectral images demonstrate that the proposed method outperforms other benchmark competitors.