Hyperspectral Anomaly Detection Based on Spatial-Spectral Cross-Guided Mask Autoencoder

Autoencoders (AEs) have gained widespread application in the field of hyperspectral anomaly detection, largely due to their notable effectiveness in efficiently reconstructing backgrounds within hyperspectral images (HSIs). However, the absence of prior knowledge and constraints imposed by spectral information capacity hinder the accuracy of anomaly detection by allowing AEs to reconstruct both anomalous targets and backgrounds simultaneously. To address this limitation, a spatial-spectral cross-guided masked autoencoder (SSCMAE) has been proposed. The guided mask is generated based on the spectral difference between the anomaly and the background. This mask effectively suppresses the reconstruction of anomalous targets while enhancing the accuracy of background reconstruction. Moreover, a dual-branch structure operates, encompassing spatial and spectral dimensions, effectively capturing the inherent three-dimensional characteristics present in HSIs. Ingeniously designed cross-connection layers within the architecture enhance the spatial and spectral branches' capability of extracting internal spatial and spectral features of images. In order to capture a more comprehensive range of background features, a lightweight three-dimensional convolutional autoencoder is introduced. This addresses the issue of local feature loss during background reconstruction and overcomes the limitations that visual transformers face when learning local image structures. The proposed method has been systematically compared against several advanced methods on six real-world datasets. The results explicitly demonstrate the efficacy and superior performance of the presented SSCMAE approach.