Generative Self-Supervised Learning With Spectral-Spatial Masking for Hyperspectral Target Detection

Deep learning (DL) has made significant progress in hyperspectral target detection (HTD) in recent years. However, the existing DL-based HTD methods generally generate numerous labeled samples for network training, which may be impure or too similar to each other. Moreover, most methods with enormous parameters are trained and tested on the same dataset, resulting in single scenario applicability and significant computational consumption issues. To solve these issues, we propose a generative self-supervised learning (GSSL) pretraining model with spectral-spatial masking (S2M). The lightweight vision transformer (ViT) is utilized as the backbone to learn the universal feature representation of images without labeled samples. Subsequently, the pretrained model is transferred to various HTD tasks. The transfer learning model is constructed via the lightweight ViT and a fully connected (FC) layer and fine-tuned via a weighted binary cross entropy (WBCE) loss function and a small number of selected samples. We evaluate its effectiveness on four challenging hyperspectral datasets in terms of the GSSL pretraining model, the S2M strategy, and the WBCE loss function. Our methods achieve improvements in comparison to different pretraining models, masking strategies and loss functions. And our detection results also outperform other state-of-the-art approaches.