用SAM生成伪标签，提升遥感图像分割精度。
SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation
- 利用SAM生成伪标签，通过自训练迭代优化。
- 在波茨坦数据集上达到85.7%的分割准确率。
- 适合标注数据少的遥感场景应用。
公开遥感数据集常因分辨率差异和地物类别定义不一致而限制通用性。为充分利用大量未标注遥感数据，我们提出SAMST，一种半监督语义分割方法。SAMST融合了分割一切模型（SAM）在零样本泛化和边界检测方面的优势，通过两个核心组件迭代优化伪标签：基于标注与伪标签数据的监督模型自训练，以及基于SAM的伪标签精炼器。该精炼器包含三个模块：阈值过滤模块用于预处理，提示生成模块提取连通区域并生成SAM提示，标签精炼模块完成最终标签拼接。通过结合大模型的泛化能力与小模型的训练效率，SAMST显著提升伪标签准确性，进而增强整体性能。在波茨坦数据集上的实验验证了SAMST的有效性与可行性，展现出应对遥感语义分割中标签数据稀缺问题的潜力。
原文摘要 · Abstract (English)
Public remote sensing datasets often face limitations in universality due to resolution variability and inconsistent land cover category definitions. To harness the vast pool of unlabeled remote sensing data, we propose SAMST, a semi-supervised semantic segmentation method. SAMST leverages the strengths of the Segment Anything Model (SAM) in zero-shot generalization and boundary detection. SAMST iteratively refines pseudo-labels through two main components: supervised model self-training using both labeled and pseudo-labeled data, and a SAM-based Pseudo-label Refiner. The Pseudo-label Refiner comprises three modules: a Threshold Filter Module for preprocessing, a Prompt Generation Module for extracting connected regions and generating prompts for SAM, and a Label Refinement Module for final label stitching. By integrating the generalization power of large models with the training efficiency of small models, SAMST improves pseudo-label accuracy, thereby enhancing overall model performance. Experiments on the Potsdam dataset validate the effectiveness and feasibility of SAMST, demonstrating its potential to address the challenges posed by limited labeled data in remote sensing semantic segmentation.
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