Abstract
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 this https URL.
Abstract (translated)
超光谱显着目标检测（HSOD）在各种应用领域的表现已经非常出色，特别是在复杂的场景中，传统基于RGB的方法会失败。尽管HSOD方法的发展取得了显著的进步，但有两个关键挑战需要立即关注。首先，现有的超光谱数据维度减缩技术会丢失光谱信息，从而影响检测精度。其次，以前的方法在特征提取过程中没有充分利用超光谱图像的固有特征。为了应对这些挑战，我们提出了一个名为去中心化混合光谱空间网络（DMSSN）的新方法，包括去中心化光谱编码过程和混合光谱空间Transformer（MSST）特征提取网络。编码过程利用知识蒸馏来构建一个轻量级的自编码器，实现稳健的编码能力和低计算成本之间的平衡。MSST通过多个注意头组提取光谱-空间特征，共同增强其对抗复杂场景的抵抗力。此外，我们还创建了一个大规模HSOD数据集HSOD-BIT，以解决该领域数据稀缺的问题，并满足深度网络训练的基本数据要求。大量实验证明，与最先进的系统相比，我们提出的DMSSN在多个数据集上都实现了卓越的表现。我们将很快将代码和数据公开发布在https://这个URL上。
URL
https://arxiv.org/abs/2404.00694
