Hyperspectral Target Detection Method Based on Nonlocal Self-Similarity and Rank-1 Tensor

In recent years, many target detection methods based on tensor representation theory have been proposed and achieved good results for hyperspectral images (HSIs). However, these methods still have some deficiencies. For example, 3-D hyperspectral data are first transformed into 1-D vectors in these methods, which may destroy the spatial structure of HSI data and reduce the detection performance. Besides, when the number of training samples is small, the results of the target detection method usually become worse. To solve these problems, a hyperspectral target detection method based on nonlocal self-similarity and rank-1 tensor is proposed in this article. First, different from these traditional tensor representation-based methods, the third-order tensor data are directly used as the input of the proposed method to preserve the spatial information and structure of an HSI. Second, the tensor blocks related to the class are constructed by using the nonlocal self-similarity of HSI data. Finally, by taking advantage of rank-1 canonical decomposition attribute, the process of tensor operation can be simplified, and the number of training samples can be reduced. The proposed method is compared with six state-of-the-art hyperspectral target detection methods on four HSI data sets. The experimental results show that the proposed method can have better target detection results than other compared methods, especially in the case of fewer training samples.