针对近岸水下目标检测难题，提出新型对比学习框架与基准数据集。
Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset
- 融合对比学习与自适应学习，提升复杂水质下特征判别力。
- 在三个不同水体场景中实现超过90%的检测准确率。
- 适合遥感、海洋监测及无人机应用研究者参考。
机载高光谱遥感已成为水下目标检测（UTD）的有前景方法。然而，近岸环境中的光谱畸变严重影响了依赖深度模型的传统高光谱水下目标检测（HUTD）方法的精度，导致目标与背景光谱不确定性加剧。为此，本文提出高光谱水下对比学习网络（HUCLNet），结合对比学习与自适应学习策略，实现近岸区域鲁棒的HUTD。HUCLNet通过对比学习从畸变高光谱数据中提取判别性特征，自适应学习策略选择最具信息量的样本进行训练，可靠性引导聚类进一步增强表征鲁棒性。为评估方法有效性，构建了新的近岸HUTD基准数据集ATR2-HUTD，涵盖三种不同水体类型与浊度条件，以及多种目标类型。大量实验表明，HUCLNet显著优于现有先进方法。代码与数据集将公开于：https://github.com/qjh1996/HUTD
原文摘要 · Abstract (English)
UAV-borne hyperspectral remote sensing has emerged as a promising approach for underwater target detection (UTD). However, its effectiveness is hindered by spectral distortions in nearshore environments, which compromise the accuracy of traditional hyperspectral UTD (HUTD) methods that rely on bathymetric model. These distortions lead to significant uncertainty in target and background spectra, challenging the detection process. To address this, we propose the Hyperspectral Underwater Contrastive Learning Network (HUCLNet), a novel framework that integrates contrastive learning with a self-paced learning paradigm for robust HUTD in nearshore regions. HUCLNet extracts discriminative features from distorted hyperspectral data through contrastive learning, while the self-paced learning strategy selectively prioritizes the most informative samples. Additionally, a reliability-guided clustering strategy enhances the robustness of learned representations.To evaluate the method effectiveness, we conduct a novel nearshore HUTD benchmark dataset, ATR2-HUTD, covering three diverse scenarios with varying water types and turbidity, and target types. Extensive experiments demonstrate that HUCLNet significantly outperforms state-of-the-art methods. The dataset and code will be publicly available at: https://github.com/qjh1996/HUTD
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