这项研究提出了一种新的方法，通过弱监督学习，从侧扫声纳图像中高效地制图海草栖息地，这对于海岸管理至关重要。与传统的手动标注方法相比，这种方法不仅速度快，而且成本低，值得一看。
本研究提出一种弱监督语义分割框架，用于侧扫声纳（SSS）海底栖息地制图，通过图像级标签学习像素级地图。框架结合基于ViT的编码器-解码器与分类分支，提取类激活图，并通过密集的条件随机场将其细化成伪标签。采用迭代自训练方案和采样策略处理数据中的强类别不平衡。研究发现，Lovász-Softmax损失函数在分割质量上最为有效。在保留的横截面上，细化后的伪标签达到89.3%的mIoU，分割分支在无任何像素级标签的情况下达到87.6%。在未标记的SSS上进行自监督预训练，平均交并比（mIoU）提高了3%。实地试验进一步证明了训练模型的泛化能力。结果表明，在海岸带海草监测所需的规模下，从侧扫声纳进行精确且标签高效的底栖栖息地制图是可行的。
Weakly Supervised Seafloor Segmentation for Seagrass Habitat Mapping in Side-Scan Sonar Imagery
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.