HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection

Abstract

Hyperspectral anomaly detection (HAD) identifies anomalies by analyzing differences between anomalies and background pixels without prior information, presenting a significant challenge. Most existing studies leverage the high correlation in spectral and spatial dimensions, primarily focusing on local spectral and spatial information for background reconstruction while neglecting long-range dependencies. This local perception constrains models from fully capturing intrinsic spatial–spectral connections. To address this, we propose a novel hybrid transformer-CNN network for HAD (HTC-HAD). Specifically, HTC-HAD combines CNNs with transformers, where the CNN focuses on local modeling, and the transformer addresses long-range modeling. This dual approach ensures the accurate reconstruction of backgrounds by capturing both local and long-range dependencies. Meanwhile, to reduce model complexity and redundancy among neighboring bands, we incorporate a simplified and effective band selection strategy as preprocessing. In addition, to prevent anomalies from being reconstructed during background estimation, we employ an adaptive weight loss function to suppress them. Experimental results on several real datasets, both qualitatively and quantitatively, demonstrate that our HTC-HAD achieves satisfying detection performance.