用于高光谱显著目标检测的光谱-空间协同引导网络
Spectral-Spatial Synergistic Guided Network for Hyperspectral Salient Object Detection
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中文总结 AI 辅助
该研究针对高光谱显著目标检测问题，提出光谱-空间协同引导网络S3GNet。核心方法包括无参数光谱结构感知模块、流感知注意力模块和渐进式门控细化解码器，实现抗光照变化、光谱-空间协作及精确边界与细节恢复，性能优于现有方法。
中文摘要 AI 辅助
高光谱显著目标检测旨在从高光谱图像中识别视觉上显著的区域。现有方法常因误解数据而失败，混淆了由外部因素如光照引起的附带光谱变化与由物体固有材料特性引起的本质光谱差异，导致表征脆弱和预测有噪声。为此，我们提出了轻量级且高效的光谱-空间协同引导网络（S3GNet），以结构感知为核心，围绕光谱稳健建模、跨流协同感知和多尺度细化解码构建闭环信息流。S3GNet引入无参数光谱结构感知模块，利用光谱导数和区域分层建模提取抗光照变化的固有特征。流感知注意力模块通过流间全局交互和流内空间引导实现有效的光谱-空间协作。此外，渐进式门控细化解码器通过最优整合多尺度特征确保精确的物体边界和细节恢复。实验结果表明，S3GNet在计算效率和检测精度方面均优于现有方法。
英文摘要
Hyperspectral salient object detection aims to identify visually salient regions from hyperspectral images. Existing methods often fail because they fundamentally misunderstand the data, confusing incidental spectral variations caused by external factors such as illumination with essential spectral differences caused by the intrinsic material properties of the object. This leads to fragile representations and noisy predictions. To this end, we propose a lightweight and efficient Spectral-Spatial Synergistic Guided Network (S3GNet), with structure perception as the core, to build a closed-loop information flow around spectrum robust modeling, cross-stream co-perception and multi-scale refinement decoding. S3GNet introduces a parameter-free Spectral Structure-Aware Module that leverages spectral derivatives and regional hierarchical modeling to extract intrinsic features of robustness against illumination variations. Our Stream-Aware Attention Module achieves effective spectral-spatial collaboration through inter-stream global interaction and intra-stream spatial guidance. Furthermore, a Progressive Gated Refinement Decoder ensures precise object boundaries and detail recovery by optimally integrating multi-scale features. Experimental results show that S3GNet achieves superior performance in both computational efficiency and detection accuracy compared to existing methods.
发表机构
- Chongqing Innovation Center, Beijing Institute of Technology（重庆创新中心，北京理工大学）
- School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）
- Key Laboratory of Photoelectronic Imaging Technology and System, Ministry of Education of China（中国教育部光电成像技术与系统重点实验室）
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