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arXiv · 2601.03736

HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection

Shuyan Bai·Tingfa Xu·Peifu Liu·Yuhao Qiu·Huiyan Bai·Huan Chen·Yanyan Peng·Jianan Li
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Abstract

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.

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Shuyan Bai, Tingfa Xu, Peifu Liu, Yuhao Qiu, Huiyan Bai, Huan Chen, Yanyan Peng, Jianan Li. 2026-01-07. HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection. https://arxiv.org/abs/2601.03736

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