TY - JOUR
T1 - HySaDe-Mamba
T2 - A Mamba-Based Network for Hyperspectral Salient Object Detection
AU - Zhang, Lei
AU - Zhang, Huijie
AU - Luo, Xiaoyan
AU - Bian, Liheng
AU - Zhen, Xiantong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2026
Y1 - 2026
N2 - 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. The source code is publicly available at https://github.com/Lee-zl/HySaDe-Mamba
AB - 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. The source code is publicly available at https://github.com/Lee-zl/HySaDe-Mamba
KW - Hyperspectral image (HSI)
KW - Mamba
KW - salient object detection (SOD)
KW - state space model (SSM)
UR - https://www.scopus.com/pages/publications/105020053386
U2 - 10.1109/TCSVT.2025.3625079
DO - 10.1109/TCSVT.2025.3625079
M3 - Article
AN - SCOPUS:105020053386
SN - 1051-8215
VL - 36
SP - 3085
EP - 3100
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 3
ER -