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.

翻译：基于RGB的伪装目标检测在现实场景中面临挑战，当颜色与纹理线索模糊不清时性能受限。高光谱图像通过捕获细粒度光谱特征提供了有力的替代方案，然而，由于缺乏专用的大规模基准数据集，高光谱伪装目标检测（HCOD）的发展受到严重制约。为激发创新，我们提出了首个面向HCOD的挑战性基准——HyperCOD。该数据集包含350幅高分辨率高光谱图像，涵盖复杂现实场景，具有目标尺寸极小、形状复杂、严重遮挡及动态光照等特点，旨在挑战现有模型。以Segment Anything Model（SAM）为代表的基础模型的出现带来了重要机遇。为将SAM适配于HCOD任务，我们提出了高光谱伪装感知SAM（HSC-SAM）。HSC-SAM通过将高光谱图像解耦为输入SAM图像编码器的空间图谱与作为自适应提示的光谱显著性图谱，实现了巧妙的模态重构。这种转换有效弥合了模态差异。大量实验表明，HSC-SAM在HyperCOD上创造了新的最优性能，并在其他公开高光谱数据集上展现出强大的泛化能力。HyperCOD数据集与我们的HSC-SAM基线为这一新兴领域的未来研究奠定了坚实基础。