HyperCOD：首个具有挑战性的基准和基线用于超光谱伪装物体检测
HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection
AI总结：
HyperCOD是首个针对超光谱伪装物体检测的挑战性基准，提出HSC-SAM方法，通过分解超光谱图像以提升检测性能。
摘要：
基于RGB的伪装物体检测在现实场景中面临困难，因为颜色和纹理线索具有歧义性。虽然超光谱图像通过捕捉细粒度的光谱特征提供了有力的替代方案，但超光谱伪装物体检测（HCOD）的进展受到缺乏专门、大规模基准的严重阻碍。为了促进创新，我们介绍了HyperCOD，首个具有挑战性的HCOD基准。它包含350张高分辨率超光谱图像，具有复杂的现实场景，包括少量物体、复杂形状、严重遮挡和动态照明，以挑战当前模型。基础模型如Segment Anything Model（SAM）的出现提供了一个有吸引力的机会。为了将SAM适应于HCOD，我们提出了HyperSpectral Camouflage-aware SAM（HSC-SAM）。HSC-SAM巧妙地将超光谱图像分解为空间地图，输入SAM的图像编码器，以及一个光谱显著性地图，作为自适应提示。这种转换有效弥合了模态差距。广泛的实验表明，HSC-SAM在HyperCOD上设定了新的最先进的性能，并且能够稳健地推广到其他公开的HSI数据集。HyperCOD数据集和我们的HSC-SAM基线为该新兴领域未来的研究提供了坚实的基础。
英文摘要：
RGB-based camouflaged object detection struggles in real-world scenarios where color and texture cues are ambiguous. While hyperspectral image offers a powerful alternative by capturing fine-grained spectral signatures, progress in hyperspectral camouflaged object detection (HCOD) has been critically hampered by the absence of a dedicated, large-scale benchmark. To spur innovation, we introduce HyperCOD, the first challenging benchmark for HCOD. Comprising 350 high-resolution hyperspectral images, It features complex real-world scenarios with minimal objects, intricate shapes, severe occlusions, and dynamic lighting to challenge current models. The advent of foundation models like the Segment Anything Model (SAM) presents a compelling opportunity. To adapt the Segment Anything Model (SAM) for HCOD, we propose HyperSpectral Camouflage-aware SAM (HSC-SAM). HSC-SAM ingeniously reformulates the hyperspectral image by decoupling it into a spatial map fed to SAM's image encoder and a spectral saliency map that serves as an adaptive prompt. This translation effectively bridges the modality gap. Extensive experiments show that HSC-SAM sets a new state-of-the-art on HyperCOD and generalizes robustly to other public HSI datasets. The HyperCOD dataset and our HSC-SAM baseline provide a robust foundation to foster future research in this emerging area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
