Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset

2025-02-20

AI Summary

To address spectral distortion and depth-model failure in unmanned aerial vehicle (UAV)-borne hyperspectral remote sensing for underwater target detection in complex coastal waters, this paper proposes HUCLNet—a hybrid hierarchical contrastive learning framework integrating self-paced learning and reliability-guided clustering—to effectively mitigate target-background confusion induced by spectral uncertainty. Furthermore, we introduce ATR2-HUTD, the first benchmark dataset specifically designed for hyperspectral underwater target detection in coastal environments. Extensive experiments demonstrate that HUCLNet achieves significant performance gains over existing state-of-the-art methods across diverse coastal water types. Both the source code and the ATR2-HUTD dataset are publicly released to foster reproducible research and serve as a foundational resource for future studies in this domain.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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

Problem

• Spectral distortions in nearshore environments
• Improving underwater target detection accuracy
• Developing a robust contrastive learning framework

Innovation

• Contrastive learning for feature extraction
• Self-paced learning for sample prioritization
• Reliability-guided clustering for robust representations