面向建筑物轮廓规则化的双路径边界约束与相对论生成对抗网络  

Two-stream boundary constraints and relativistic generation adversarial network for building contour regularization

作　　者：殷吉崇 武芳[1] 翟仁健[1] 邱越 巩现勇[1] 行瑞星 YIN Jichong;WU Fang;ZHAI Renjian;QIU Yue;GONG Xianyong;XING Ruixing(Institute of Geospatial Information,Information Engineering University,Zhengzhou 450001,China)

机构地区：[1]信息工程大学地理空间信息学院,河南郑州450001

出　　处：《测绘学报》2024年第7期1444-1457,共14页Acta Geodaetica et Cartographica Sinica

基　　金：国家自然科学基金(42201491);河南省杰出青年科学基金(212300410014)。

摘　　要：高分辨率遥感影像建筑物提取目前仍是遥感应用与地图制图领域的研究热点和难点。尽管深度学习方法的引入极大地提升了建筑物分割的精度,但建筑物分割掩膜中轮廓不规则和边界不清晰的问题依然存在。为了获取规则的建筑物轮廓和清晰的边界,本文基于双路径边界约束与相对论生成对抗网络提出一种建筑物轮廓规则化方法。该网络由双路径边界约束生成器和相对论平均鉴别器共同组成。双路径边界约束生成器通过双路径网络架构和边界损失函数来融合遥感影像和输入标签的边界细节信息来,从而生成规则的建筑物轮廓;而相对论平均鉴别器则通过评估地面实况标签与生成的规则化结果之间的质量差异来迫使生成器生成更为真实的建筑物掩膜。为验证模型性能、探索性能提升原因,本文在WHU建筑物数据集和Inria航空影像标注数据集上设计了对比试验和消融试验。试验结果表明,本文方法可以生成吻合地面实况标签的规则化结果,在解决分割掩膜边界模糊、轮廓不规则的问题上具有显著优势。Building extraction from high-resolution remote sensing images is still a hot and difficult research topic in the field of remote sensing application and cartography.Although the introduction of deep learning method has greatly improved the accuracy of building segmentation,the problems of irregular contour and unclear boundary in building segmentation mask still exist.In order to obtain regular building contours and clear boundaries,this paper proposes a method of building contour regularization based on two-stream boundary constraint and relativistic generation adversarial network.The network is composed of a two-stream boundary constraint generator and a relativistic average discriminator.The two-stream boundary constraint generator fuses the boundary details of remote sensing images and input labels through the two-stream network architecture and boundary loss function,thus generating regular building contours.The relativistic average discriminator forces the generator to generate a more realistic building mask by evaluating the quality difference between the ground truth label and the generated regularization result.To verify the performance of the model and explore the reasons for the performance improvement,two groups of experiments were designed on WHU building dataset and Inria aerial image labeling dataset,including a comparative experiment and ablation experiment.The experimental results show that this method can generate regularization results that match ground truth labels,and it has obvious advantages in solving the problems of blurred boundaries and irregular contours of segmentation masks.

关 键 词：建筑物轮廓规则化 边界约束 生成对抗网络 高分辨率遥感影像 

分 类 号：P283[天文地球—地图制图学与地理信息工程]