用地形先验和自监督提升洞穴分割精度，接近人工标注效果
SinkSAM-Net: Knowledge-Driven Self-Supervised Sinkhole Segmentation Using Topographic Priors and Segment Anything Model
- 结合地形特征与SAM模型，通过坐标扰动生成伪标签
- 迭代优化轻量模型，实现95%人工标注水平的分割性能
- 适合大规模无标注遥感图像洞穴检测，推理快且参数少
土壤塌陷显著影响土壤退化、基础设施脆弱性和地貌演化。然而，其不规则形状及阴影、植被干扰使遥感数据中准确量化其属性极为困难。人工标注成本高、耗时长。本文提出一种新型自监督框架SinkSAM-Net，融合传统闭合洼地地形计算与迭代式几何感知提示的Segment Anything Model（SAM）。通过单目深度信息与坐标级边界框抖动（CWBJ）增强技术，对洞穴边界进行像素级精修，生成高质量伪标签。这些伪标签用于迭代训练轻量级EfficientNetV2-UNet模型，并最终将知识迁移至无需提示、参数少、推理快的模型。在包含多种成因洞穴的大规模数据库上，使用航空与高分辨率无人机影像评估，该方法性能达人工标注的约95%。这是首个自监督洞穴分割框架，证明了基础模型（如SAM和Depth Anything V2）结合地形与几何先验及迭代自学习流程的鲁棒性。该框架可有效训练于海量未标注RGB洞穴数据集，达到与监督模型相当的效果。代码与交互演示见https://osherr1996.github.io/SinkSAMNet
原文摘要 · Abstract (English)
Soil sinkholes significantly influence soil degradation, infrastructure vulnerability, and landscape evolution. However, their irregular shapes, combined with interference from shadows and vegetation, make it challenging to accurately quantify their properties using remotely sensed data. In addition, manual annotation can be laborious and costly. In this study, we introduce a novel self-supervised framework for sinkhole segmentation, termed SinkSAM-Net, which integrates traditional topographic computations of closed depressions with an iterative, geometry-aware, prompt-based Segment Anything Model (SAM). We generate high-quality pseudo-labels through pixel-level refinement of sinkhole boundaries by integrating monocular depth information with random prompts augmentation technique named coordinate-wise bounding box jittering (CWBJ). These pseudo-labels iteratively enhance a lightweight EfficientNetV2-UNet target model, ultimately transferring knowledge to a prompt-free, low-parameter, and fast inference model. Our proposed approach achieves approximately 95\% of the performance obtained through manual supervision by human annotators. The framework's performance was evaluated on a large sinkhole database, covering diverse sinkhole dateset-induced sinkholes using both aerial and high-resolution drone imagery. This paper presents the first self-supervised framework for sinkhole segmentation, demonstrating the robustness of foundational models (such as SAM and Depth Anything V2) when combined with prior topographic and geometry knowledge and an iterative self-learning pipeline. SinkSAM-Net has the potential to be trained effectively on extensive unlabeled RGB sinkholes datasets, achieving comparable performance to a supervised model. The code and interactive demo for SinkSAM-Net are available at https://osherr1996.github.io/SinkSAMNet
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