A novel noise-robust hyperspectral anomaly detector based on relative total variation collaborative representation is proposed to settle the problem of low detection probability of collaborative representation detector under the condition of noisy hyperspectral image or large and irregular anomaly. The relative total variation method is employed to preprocess hyperspectral image and to obtain the pure structure information of hyperspectral image, which features lower intra-class difference and higher inter-class difference. Subsequently, the collaborative representation detector can be carried out, effectively alleviating the abnormal contamination of local background. Superior anomaly detection performance is obtained by the proposed algorithm, and the dependent of anomaly detection accuracy on the size of double-windows is greatly reduced.
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