Global Feature-Injected Blind-Spot Network for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) poses the challenge of distinguishing anomalous targets from the majority of background objects without prior knowledge. Most existing deep learning (DL) models struggle to account for both local and global spatial-spectral features in the image, limiting their performance. In this letter, we introduce PUNNet, which integrates the patch-shuffle downsampling technique and nonlinear activation-free network (NAFNet) block with dilated convolution into an advanced blind-spot network for HAD. Specifically, PUNNet utilizes the patch-shuffle downsampling operation to extend its receptive field and exploits channel attention in the NAFNet block with dilated convolution to capture global contextual information in the image. Meanwhile, PUNNet satisfies the blind-spot requirement, meaning its receptive field excludes the center pixel's information. This allows for reliable and precise background reconstruction in a self-supervised learning paradigm, further weakening anomalous feature expression and increasing the reconstruction error of anomalies. Experimental results demonstrate that PUNNet achieves a leading position in HAD performance. The code is available at https://github.com/DegangWang97/IEEE_GRSL_PUNNet.