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

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 -