无需精确标签对齐，就能实现高质量建筑物分割与自动校准。
Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels
- 用仿射变换动态调整错位标签，提升分割模型训练目标质量。
- 自监督正则化防止模型依赖错误标签，显著提升精度。
- 完全无须真实标注配对，适合遥感图像中标签来源不一致场景。
图像分割的监督学习通常要求图像与标签在空间上严格对齐。当图像与标签来自不同来源时，配对可能错位，严重降低模型性能，这在遥感领域尤为常见——如航空或卫星图像与来自OpenStreetMap等源的标签进行配准。本文提出一种新方法AnS（Align and Segment），可同时学习标签对齐与分割。该方法基于空间变换模块，通过仿射变换调整错位标签，为标准语义分割网络提供更优的学习目标。通过自监督正则化损失防止模型学习到错误的捷径，且该策略与数据增强互补，尤其适用于系统性错位的数据。关键优势在于无需任何真实标注配对即可训练。我们在多个城市的真实与合成数据上验证，该方法能同时实现高质量的建筑物分割和精确的标签-图像对齐。代码与衍生数据集已开源。
原文摘要 · Abstract (English)
Supervised learning for image segmentation typically requires spatially aligned image and label sets. When images and labels originate from different sources, the pairing may be misaligned, which can significantly deteriorate the performance of the learned models. This is especially common in remote sensing, when aerial or satellite images are co-registered with labels from another source (e.g., OpenStreetMap). In this work, we propose a novel approach for training on misaligned labels, where we simultaneously learn the label alignment. Our align and segment (AnS) approach builds on the spatial transformer module to transform the misaligned labels using an affine transformation to provide a better learning target for a canonical semantic segmentation network. We prevent shortcut learning of misaligned labels in these semantic segmentation networks through a self-supervised regularization loss and show that it is complementary to data augmentation, especially for systematically misaligned training data. A decisive characteristic of our AnS approach is that it learns without requiring any golden labels. We experimentally show on both synthetic and real-world data from different cities that our approach enables high-quality building segmentation and precise label-image alignment at the same time. Code and derived datasets are available at https://github.com/venkanna37/align-and-segment
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