构建首个大规模高光谱显著性检测挑战基准，解决小目标与相似色难题。
HSOD-BIT-V2: A New Challenging Benchmarkfor Hyperspectral Salient Object Detection
- 设计五类挑战场景，聚焦小目标与背景相似性问题。
- 提出Hyper-HRNet模型，融合全局信息与光谱自相似特征，提升定位精度。
- 在复杂场景中显著超越现有方法，适合遥感与医学图像分析研究者。
显著性检测（SOD）在计算机视觉中至关重要，但基于RGB的方法在小目标和颜色相近等复杂场景中表现受限。高光谱图像凭借丰富的光谱信息，为更精准的高光谱显著性检测（HSOD）提供了可能，然而现有方法受限于缺乏广泛可用的数据集。为此，本文提出目前最大且最具挑战性的HSOD基准数据集HSOD-BIT-V2。该数据集设计了五类挑战，重点针对小目标和前景-背景相似性问题，突出光谱优势与真实世界复杂性。为应对这些挑战，我们提出Hyper-HRNet——一种高分辨率的HSOD网络。该模型通过捕捉光谱自相似特征有效提取、整合并保留关键光谱信息，同时降低维度；并通过融合全局信息与精细的物体显著性表示，准确传递细节并精确定位目标轮廓。实验表明，Hyper-HRNet在复杂场景下显著优于现有模型。
原文摘要 · Abstract (English)
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.
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