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

TL;DR

HyperCOD introduces the first large-scale hyperspectral camouflaged object detection benchmark with complex scenarios, and proposes HSC-SAM, a novel adaptation of SAM, achieving state-of-the-art results and fostering future research.

Contribution

The paper presents HyperCOD, the first challenging hyperspectral camouflaged object detection benchmark, and introduces HSC-SAM, a novel model adapting SAM for hyperspectral data.

Findings

HSC-SAM achieves new state-of-the-art on HyperCOD.

HyperCOD dataset contains 350 high-resolution hyperspectral images.

HSC-SAM generalizes well to other hyperspectral datasets.

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.