Proxy-Enhanced Prototype Memory Network for Weakly Supervised Hyperspectral Target Detection

Bo Yang, Jinjian Wu, Changzhe Jiao

Abstract: Hyperspectral target detection (HTD) holds significant promise in numerous earth vision applications, yet it encounters challenges in acquiring high-quality prior target signatures, capturing target spectral variability, and dealing with sample imbalance. To address these issues, we propose a weakly supervised solution, the proxy-enhanced prototype memory network (PE-PMN), for HTD tasks. It relies solely on region-level weakly labeled data, eliminating the need for strict prior target knowledge (e.g., handcrafted target signatures or pixel-level annotations). To fully describe target variations and background diversity, two memory prototype networks are introduced to extract, store, and retrieve prototypes of targets and backgrounds, providing comprehensive spectral information. Additionally, a proxy-based enhancement approach is incorporated to enrich the prototypes in the memory banks and boost the separation between target and background features. To mitigate sample imbalance in the PE-PMN, we developed the bag mix-up (BMU) strategy based on the unconstrained linear mixture model (ULMM) to construct a sufficient training dataset. Experimental results on three simulated datasets and three real datasets demonstrate that the proposed PE-PMN significantly outperforms other competitive weakly supervised HTD methods.