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Hyperspectral anomaly detection, as an important application of hyperspectral remote sensing, is widely used in mineral exploration, environmental monitoring, military reconnaissance, etc. The anomalies in hyperspectral image mainly refer to the spectral anomalies caused by the reflection or radiation of some special objects. They have the characteristics of small scales, no prior information, and low probability of occurrence. Such anomalies in the military, agriculture, geology and other fields often contain much important information and has great application value. The sparsity score estimation algorithm is a global algorithm with high stability but as well as high false alarm rate. In this paper, we propose a hyperspectral anomaly detection algorithm based on non-negative sparsity score estimation. Firstly, the initial dictionary is obtained by K-SVD algorithm. After the sparse representation model of hyperspectral image is generated by orthogonal matching pursuit algorithm, dictionary atoms and corresponding coefficients are updated through singular value decomposition of the error term, then the optimization is achieved by successive iteration. Secondly, the nonnegative constraint condition is introduced into the sparse representation model, and the non-negative sparse coefficient is solved by the non-negative sparse coding. Finally, the non-negative sparse coefficients are used to calculate the atom usage probability, so as to infer whether the corresponding image pixels are anomaly or not. The experiments conducted on hyperspectral images show that the proposed method is superior to some typical methods, which has a lower false alarm rate while remains the accuracy.
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