PolyBuild: 一种从高分辨率遥感图像中提取多边形建筑物轮廓的端到端方法
PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
AI总结：
提出端到端方法PolyBuild，通过初始轮廓生成模块和轮廓优化模块直接从遥感图像提取矢量多边形建筑物轮廓，无需后处理，性能优于现有方法。
摘要：
从高分辨率遥感图像中提取建筑物多边形轮廓是各种地图应用的基本任务。然而，不同的成像条件和复杂的建筑结构使得自动轮廓提取极具挑战性。主流的建筑物提取方法通常依赖于像素级分割，随后进行多个后处理步骤以生成建筑物轮廓，这计算量大且容易出错。在本文中，我们提出了一种名为PolyBuild的端到端方法，该方法可以直接从高分辨率遥感图像中提取建筑物矢量多边形，无需任何后处理操作。该方法利用两个主要模块：初始轮廓生成模块（ICGM）和轮廓优化模块（COM）。ICGM通过利用每个建筑物实例的拼接子区域中心特征来生成初始建筑物轮廓。它通过生成边界框并使用四个子区域的中心特征来表示每个建筑物，同时进行目标检测和初始轮廓提取。轮廓优化模块（COM）通过在基于Transformer的解码器中迭代集成卷积神经网络（CNN）特征和轮廓位置信息，进一步细化生成的建筑物轮廓。混合CNN-Transformer架构有效捕获建筑物轮廓内的局部和全局空间关系，确保高质量的边界描绘。在三个建筑物数据集上进行了大量实验以评估PolyBuild的性能。结果表明，PolyBuild显著优于最先进的方法，包括基于掩码和基于轮廓的方法。
英文摘要：
Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. Mainstream approaches for building extraction often rely on pixel-level segmentation followed by multiple post-processing steps to produce building contour, which can be computationally intensive and prone to errors. In this paper, we propose an end-to-end method named PolyBuild, which can directly extract building vector polygons from high-resolution remote sensing images without the need for any post-processing operations. The proposed method leverages two primary modules: an Initial Contour Generation Module (ICGM) and a Contour Optimization Module (COM). The ICGM is designed to generate an initial building contour by utilizing concatenated sub-region center features for each building instance. It performs simultaneous object detection and initial contour extraction by generating bounding boxes and using the center features of four sub-regions to represent each building. The Contour Optimization Module (COM) further refines the generated building contours by iteratively integrating Convolutional Neural Network (CNN) features and contour positional information in a Transformer-based decoder. The hybrid CNN-Transformer architecture effectively captures both local and global spatial relationships within the building contour, ensuring high-quality boundary delineation. Extensive experiments are conducted on three building datasets to evaluate the performance of PolyBuild. The results demonstrate that PolyBuild significantly outperforms state-of-the-art methods, including mask-based and contour-based approaches.
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