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SPG-OD: Spectral-Prior-Guided Object Detection for Hyperspectral Remote Sensing Images
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DOI：10.1109/tgrs.2026.3717580.png)
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Abstract
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With the rapid increase in hyperspectral images (HSIs), hyperspectral object-level detection (HOD) has become an important task. However, existing methods often neglect the material similarity between the objects and the background, which increases the difficulty of object–background discrimination. Meanwhile, they have not sufficiently exploited the intrinsic advantage of HSIs, namely their continuous full-spectrum representation, which jointly captures global spectral context, local spectral variations, and dependencies across bands. To address these limitations, this article proposes the Spectral-Prior-Guided Object Detection (SPG-OD) method by integrating spectral prior information. We introduce two key modules: the grouped local spectral enhancement module (GLSEM), which enhances local spectral variations to improve object–background discrimination when objects and background share similar materials, and the spectral objectness prior module (SOPM), which uses prior spectral curves (PSCs) to guide detection. The experimental results on three large-scale HOD datasets show that SPG-OD achieves competitive overall performance and demonstrates the effectiveness of incorporating spectral prior information for hyperspectral object detection.
Keywords:
Hyperspectral image (HSI)
object detection
spectral prior information
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