A Signature-Constrained Two-Stage Framework for Hyperspectral Target Detection Based on Generative Self-Supervised Learning

Recently, the hyperspectral target detection (HTD) technique based on deep learning (DL) has been developed rapidly. However, existing algorithms show poor generalization across different hyperspectral images (HSIs), where repeated training and inference based on limited prior information are necessary. To liberate HTD from dependence on the quantity and the quality of training samples, this article proposes a signature-constrained two-stage framework for HTD (HTD-STF) based on generative self-supervised learning (GSSL). In the first stage for pretraining, to realize spectral-spatial reconstruction, we build an asymmetric autoencoder (AE) employing transformer blocks with long-range perception for generalized feature extraction. During pretraining, the spectral-spatial similarity loss (SSSL) is designed to improve the effect of reconstruction. In the second stage for fine-tuning and detection, the signature is utilized in preprocessing, training, and inference, respectively. Specifically, the coarse sample mining and tiling strategy in preprocessing not only facilitates the framework in flexible input dimension, but also provides pseudo-labels for end-to-end training. During training, we adopt the signature as guidance for feature-level fusion, which alleviates the impact of sample imbalance. After training, the final inference based on pixel-level fusion refines the original output. For ideal GSSL, the HyperMix-10K, a new large-scale hyperspectral dataset, has been constructed in this work, which contains numerous unlabeled HSIs captured in various scenes. Experimental results and analysis on real HSIs verify the effectiveness and generalization ability of the HTD-STF.