In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

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Window-based operation is a general technique for hyperspectral anomaly detection. However, the problem remains that background knowledge containing abnormal information often affects the attributes of test pixels. In this paper, a dual collaborative representation (DCR)-based hyperspectral anomaly detection method is proposed to solve the above problem effectively, which consists of the following main steps. First, lowrank and sparse matrix decomposition (LRSMD) is employed to obtain a low-rank background matrix. Then, the density peak (DP) clustering algorithm is applied to the low-rank background matrix to calculate the density information of the pixels in a sliding dual window. Specifically, pixels with the highest density are selected as the pure background pixel set to approximately represent the test pixels in this work. Next, the test pixels are approximated by the linear combination of pixels in the inner window. Finally, a decision function based on the residuals of this dual-stage collaborative representation is utilized to detect abnormal pixels. Experimental results on several hyperspectral datasets demonstrate that the proposed DCR method can both improve the separability between abnormal pixels and their corresponding background and show better detection performance with respect to state-of-the-art anomaly detection methods in terms of detection accuracy.

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