MACNet: A Multiscale Attention-Guided Contextual Network for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection (HAD) aims to identify anomalous targets that differ from the background in high-dimensional spectral images, and is widely applied in fields such as military reconnaissance and environmental monitoring. However, the diversity of anomaly scales, interference from complex backgrounds, and redundancy of spectral information pose significant challenges to achieving high detection accuracy. To address these issues, this letter proposes a multiscale attention-guided context network (MACNet) to enhance the perception of anomalous regions. MACNet consists of three components: a multiscale local feature extractor (MSLFE) that effectively captures edge structures and subtle anomalies at different scales, a global context awareness module (GCAM) that fuses local and global contextual information to improve discrimination under complex backgrounds, and a refined reconstruction and contrast enhancement module (RRCE) that employs channel attention and spatial reconstruction mechanisms to enhance the response differences between anomalies and background. Experiments on four publicly available hyperspectral datasets demonstrate that MACNet achieves superior detection accuracy compared to existing mainstream methods, validating the effectiveness of the proposed approach.