Hyperspectral Target Detection Based on Generative Self-Supervised Learning With Wavelet Transform

Recently, generative self-supervised learning (GSSL) has gained extensive attention in hyperspectral remote sensing. For the hyperspectral target detection (HTD) task, traditional GSSL-based algorithms usually require hyperspectral images (HSIs) as additional datasets for pretraining, which are relatively resource-intensive and time-consuming. To better interpret the spectral-spatial information of HSIs while alleviating the dependence on large-scale hyperspectral datasets, we develop a novel two-stage framework for HTD based on GSSL in this article. In the preprocessing for the input HSI, a dimensional transformation (DT) module and a coarse detection reference (CDR) module are constructed to produce feature patches as training samples for subsequent pretraining and fine-tuning. In the pretraining stage for spectral-spatial reconstruction, we construct an asymmetric autoencoder (AE) architecture which leverages the transformer blocks with long-range perception to extract generalized features and explore discriminative feature representations of the input HSI. Specifically, a dual-stream wavelet patch embedding (DWPE) module is proposed to integrate the wavelet transform (WT) mechanism with the convolutional neural networks (CNNs), which extracts robust spectral-spatial features by performing convolutional operations with different frequency components of WT. In the fine-tuning stage, a novel signature-constrained cross-entropy (SC-CE) loss function is proposed to constrain the network optimization. For the final detection, a pixel-level fusion based on coarse detection based pixel-level fusion (CDPF) module is employed after inference to further suppress the interference from background. Experimental results on six real HSIs demonstrate that the proposed method achieves superior detection performance while maintaining the generalization of the pretrained model.