HSOD-BIT-V2: A New Challenging Benchmarkfor Hyperspectral Salient Object Detection

• URL: http://arxiv.org/abs/2503.13906v1
• Date: Tue, 18 Mar 2025 05:09:42 GMT
• Title: HSOD-BIT-V2: A New Challenging Benchmarkfor Hyperspectral Salient Object Detection
• Authors: Yuhao Qiu, Shuyan Bai, Tingfa Xu, Peifu Liu, Haolin Qin, Jianan Li,
• Abstract summary: We introduce HSOD-BIT-V2, the largest and most challenging HSOD benchmark dataset to date.<n>We propose Hyper-HRNet, a high-resolution HSOD network.<n>It effectively extracts, integrates, and preserves effective spectral information while reducing dimensionality by capturing the self-similar spectral features.<n>It conveys fine details and precisely locates object contours by incorporating comprehensive global information and detailed object saliency representations.
• Score: 12.1018751772293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Salient Object Detection (SOD) is crucial in computer vision, yet RGB-based methods face limitations in challenging scenes, such as small objects and similar color features. Hyperspectral images provide a promising solution for more accurate Hyperspectral Salient Object Detection (HSOD) by abundant spectral information, while HSOD methods are hindered by the lack of extensive and available datasets. In this context, we introduce HSOD-BIT-V2, the largest and most challenging HSOD benchmark dataset to date. Five distinct challenges focusing on small objects and foreground-background similarity are designed to emphasize spectral advantages and real-world complexity. To tackle these challenges, we propose Hyper-HRNet, a high-resolution HSOD network. Hyper-HRNet effectively extracts, integrates, and preserves effective spectral information while reducing dimensionality by capturing the self-similar spectral features. Additionally, it conveys fine details and precisely locates object contours by incorporating comprehensive global information and detailed object saliency representations. Experimental analysis demonstrates that Hyper-HRNet outperforms existing models, especially in challenging scenarios.

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