Spectral-constrained global and local feature learning for hyperspectral anomaly detection

Hyperspectral anomaly detection (HAD) plays a crucial role in remote sensing image processing. Many autoencoder (AE)-based algorithms often face limitations due to insufficient spectral properties and inadequate integration of global and local features within the hyperspectral image (HSI). To address these challenges, a spectral-constrained global and local feature learning network (SGLNet) is proposed for HAD. Firstly, SGLNet employs three sub-networks to extract the global features, local features and spectral low-rank features from the encoding features, respectively. Specifically, a memory matrix in the low-rank representation branch can capture the global low-rank characteristics of HSI. For the global feature extraction branch, we employ graph convolution to effectively mine global information, thereby enhancing the capability of SGLNet for background modeling. Then, to make full use of the extracted features, a spectral-guided feature fusion module (SFFM) is designed to integrate the features. The SFFM can dynamically adjust local and global features while reducing spatial and spectral information redundancy, thereby enabling effective feature fusion. Next, the fused features are used to predict the background of HSI. Finally, abnormal scores are obtained by combining the RX detection result on the input HSI and the detection result using Mahalanobis distance on the residual image. Comparative experiments conducted on four real hyperspectral datasets demonstrate the effectiveness and superiority of the proposed method, surpassing previous AE-based methods by an average of 0.16%, 0.38%, 0.01%, and 0.98% in ${\mathrm{AUC}}_{\mathrm{(D,F)}}$ values. This indicates that effectively utilizing both local and global information, along with spectral properties, can enhance the accuracy of anomaly detection. The code of this work will be released at: https://github.com/xautzhaozhe/SGLNet.