  Hyperspectral salient object detection (HSOD) has exhibited remarkable
promise across various applications, particularly in intricate scenarios where
conventional RGB-based approaches fall short. Despite the considerable progress
in HSOD method advancements, two critical challenges require immediate
attention. Firstly, existing hyperspectral data dimension reduction techniques
incur a loss of spectral information, which adversely affects detection
accuracy. Secondly, previous methods insufficiently harness the inherent
distinctive attributes of hyperspectral images (HSIs) during the feature
extraction process. To address these challenges, we propose a novel approach
termed the Distilled Mixed Spectral-Spatial Network (DMSSN), comprising a
Distilled Spectral Encoding process and a Mixed Spectral-Spatial Transformer
(MSST) feature extraction network. The encoding process utilizes knowledge
distillation to construct a lightweight autoencoder for dimension reduction,
striking a balance between robust encoding capabilities and low computational
costs. The MSST extracts spectral-spatial features through multiple attention
head groups, collaboratively enhancing its resistance to intricate scenarios.
Moreover, we have created a large-scale HSOD dataset, HSOD-BIT, to tackle the
issue of data scarcity in this field and meet the fundamental data requirements
of deep network training. Extensive experiments demonstrate that our proposed
DMSSN achieves state-of-the-art performance on multiple datasets. We will soon
make the code and dataset publicly available on
https://github.com/anonymous0519/HSOD-BIT.
