用于侧扫声呐图像中海草生境制图的弱监督海底分割

中文摘要 AI 辅助

海草床是重要的蓝碳生境，绘制其分布范围是海岸带管理和碳储量清查的前提。光学卫星传感器覆盖范围广，但无法探测深水或浑浊水域，而侧扫声呐（SSS）可对任意深度的海底进行高分辨率成像。然而，侧扫声呐图像的解译仍依赖密集的人工标注，既缓慢又昂贵。为解决该问题，本文将弱监督语义分割框架应用于侧扫声呐底栖生境制图，仅通过图像级标签即可学习像素级地图。该框架将基于ViT的编解码器与分类分支耦合，提取类别激活图，并通过针对声学图像的噪声和弱边界调整的密集条件随机场将其细化为伪标签；同时采用迭代自训练方案，搭配采样策略以应对数据中严重的类别不平衡问题。本文还研究了不同损失函数对分割质量的影响，发现Lovász-Softmax损失效果最佳。在保留的样带上，经细化的伪标签与真实值相比达到89.3%的mIoU，未使用任何像素级标签训练的分割分支达到87.6%；在未标注侧扫声呐数据上进行自监督预训练，使平均交并比进一步提升3%。野外试验进一步验证了训练模型的泛化能力。这些结果表明，从侧扫声呐进行准确且标签高效的底栖生境制图，可满足海岸带海草监测所需的规模。

英文摘要

Seagrass meadows are crucial blue-carbon habitats, and mapping their extent is a prerequisite for coastal management and carbon inventory. Optical satellite sensors cover large areas but cannot reach deep or turbid water, whereas side-scan sonar (SSS) images the seabed at high resolution and at any depth. Interpreting SSS, however, still relies on dense manual annotation, which is slow and costly. We address this by adapting a weakly supervised semantic segmentation framework to SSS benthic habitat mapping, so that pixel-level maps are learned from image-level labels alone. The framework couples a ViT-based encoder-decoder with a classification branch, extracts class activation maps, and refines them into pseudo-labels with a dense conditional random field that we tune for the noise and weak boundaries of acoustic imagery. It follows an iterative self-training scheme, together with a sampling strategy to cope with the strong class imbalance of the data. We also study the effect of different loss functions on segmentation quality, finding Lovász-Softmax loss the most effective. On a held-out transect, the refined pseudo-labels reached an mIoU of 89.3\% against the ground truth, and the segmentation branch, trained without any pixel-level labels, reached 87.6\%. Self-supervised pretraining on unlabelled SSS added a further 3\% in mean intersection-over-union. Field trials further demonstrate the generalizability of the trained model. These results show that accurate and label-efficient benthic habitat mapping from side-scan sonar is feasible at the scale needed for coast-wide seagrass monitoring.

发表机构

• Computer Vision and Robotics Research Institute (ViCOROB)（计算机视觉与机器人研究学院（ViCOROB））
• University of Girona（赫罗纳大学）

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