HySaDe-Mamba: A Mamba-based Network for Hyperspectral Salient Object Detection

Hyperspectral salient object detection (HSOD) aims to identify visually and spectrally distinctive regions in hyperspectral images (HSIs). However, existing HSOD methods often suffer from spectral redundancy and inefficient spatial-spectral modeling, which hinder their scalability and accuracy in complex scenes. To tackle these challenges, we propose HySaDe-Mamba, a novel HSOD framework built upon the Mamba architecture. Specifically, to address information redundancy in HSI, we design a spatial-enhanced spectral-embedding (SeSe) module, which maps high-dimensional data into a more compact but effective representation. On the compact SeSe representation features, we further propose a Bi-scale spatial and Bi-directional spectral (BsBd) Mamba module, performing the selective scanning mechanism in a spatial-spectral hybrid, end-to-end way, which not only facilitates comprehensive spatial structural interaction across both global and local scales, but also effectively exploits the underlying spectral semantic correlation. Extensive experiments on two public HSOD datasets demonstrate that our HySaDe-Mamba achieves state-of-the-art detection accuracy across seven metrics, while maintaining an efficient inference speed of 40.22 FPS.