Transferable network with Siamese architecture for anomaly detection in hyperspectral images

•The anomaly detection problem is transformed into the similarity metric learning problem.•An adaptive unsupervised clustering process is proposed to generate pseudo labels from unlabeled images.•Pre-training and Fine-tuning strategies are adopted to ensure the transfer capability of the network. The purpose of hyperspectral anomaly detection is to distinguish abnormal objects from the surrounding background. In actual scenes, however, the complexity of ground objects, the high-dimensionality of data and the non-linear correlation of bands have high requirements for the generalizability, feature extraction ability and nonlinear expression ability of anomaly detection algorithms. In order to address the above problems, we propose a transferable network with Siamese architecture for hyperspectral image anomaly detection (TSN-HAD). The contribution of TSN-HAD is three-fold. First, we address the anomaly detection problem through similarity metric evaluation. The similarity scores are estimated with the proposed Siamese network, which is able to identify pixels that deviate from the neighboring pixels. Second, a spectral-angle-based contrastive constraint is proposed to compel the network to extract more discriminative features, which can enhance the generalizability of the network effectively. Third, to enhance the transfer capability of the network, an unsupervised adaptive clustering method is employed to fine-tune the network with pseudo-labels learned from the test image without any prior knowledge. Comparisons with the state-of-the-art methods on multiple datasets demonstrate the excellence of the proposed method.