Abstract
Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant spectral information, while HSOD methods are hindered by the lack of extensive and available datasets. In this context, we introduce HSOD-BIT-V2, the largest and most challenging HSOD benchmark dataset to date. Five distinct challenges focusing on small objects and foreground-background similarity are designed to emphasize spectral advantages and real-world complexity. To tackle these challenges, we propose Hyper-HRNet, a high-resolution HSOD network. Hyper-HRNet effectively extracts, integrates, and preserves effective spectral information while reducing dimensionality by capturing the self-similar spectral features. Additionally, it conveys fine details and precisely locates object contours by incorporating comprehensive global information and detailed object saliency representations. Experimental analysis demonstrates that Hyper-HRNet outperforms existing models, especially in challenging scenarios.
Abstract (translated)
目标显著性检测（SOD）在计算机视觉领域至关重要，然而基于RGB的方法在处理小物体和颜色特征相似的复杂场景时存在局限。高光谱图像通过提供丰富的光谱信息为更准确的目标显著性检测（HSOD）提供了有前景的解决方案，但HSOD方法的发展受到可用数据集稀缺的影响。在此背景下，我们引入了HSOD-BIT-V2，这是迄今为止规模最大、挑战性最强的HSOD基准数据集。该数据集中设计了五个不同的挑战，重点关注小物体和前景背景相似度问题，以突出光谱信息的优势及现实世界的复杂性。为应对这些挑战，我们提出了高分辨率HSOD网络Hyper-HRNet。Hyper-HRNet能够有效提取、整合并保留有效的光谱信息，并通过捕获自我相似的光谱特征来降低维度。此外，该网络还通过融合全面的全局信息和详细的物体显著性表示来传达精细细节，并精确定位物体轮廓。实验分析表明，在具有挑战性的场景下，Hyper-HRNet优于现有的模型表现。
URL
https://arxiv.org/abs/2503.13906
