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

Source: [HyperCOD: The First Challenging Benchmark and Baseline for Hyperspectral Camouflaged Object Detection](<https://www.sota2.com/research/paper/695f39e753fdd5c361743e13>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-09T05:00:05.326Z

Authors: Shuyan Bai, Tingfa Xu, Peifu Liu, Yuhao Qiu, Huiyan Bai, Huan Chen, Yanyan Peng, Jianan Li

Published: 2026-01-07T09:26:32.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Camouflaged Object Detection on HyperCOD](<https://www.sota2.com/research/sota/camouflaged-object-detection-on-hypercod.md>): Camouflaged Object Detection; HyperCOD; MAE: 0.0017.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2601.03736>)

[PDF](<https://arxiv.org/pdf/2601.03736.pdf>)