结合边界约束网络和分水岭分割算法的建筑物提取  

Building detection based on a boundary-regulated network and watershed segmentation

作　　者：罗壮 李明 张德朝 LUO Zhuang;LI Ming;ZHANG Dezhao(College of Mathematics,Taiyuan University of Technology,Jinzhong 030600,China;College of Data Science,Taiyuan University of Technology,Jinzhong 030600,China;Department of Earth System Science,Tsinghua University,Beijing 100084,China)

机构地区：[1]太原理工大学数学学院,晋中030600 [2]太原理工大学大数据学院,晋中030600 [3]清华大学地球系统科学系,北京100084

出　　处：《遥感学报》2022年第7期1459-1468,共10页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:11771321,11901423);GF-7卫星城市建设典型地物要素变化检测技术项目(编号:06-Y20A17-9001-17/18);基于高分七号卫星数据的建筑物/构筑物快速识别技术项目(编号:30-Y20A15-9003-17/18)。

摘　　要：城市作为高密度建筑区域,在较小范围内有大量结构相似的建筑紧密分布。当前从高分辨率图像中准确检测建筑仍然是一个挑战,本文受边缘检测网络启发,提出一种强化边界精度的建筑物提取新方案,根据建筑物及边界特点改进深度网络,结合自下而上分组的分水岭分割提高分类精度和建筑边界的准确度。首先对数据预处理,生成建筑边界和建筑分割线两类辅助标签;改进性能较优的建筑检测框架ICT-Net网络,修改网络结构和损失函数,针对两类辅助标签,强化边界影响,提高网络性能;最后对网络预测结果应用结合分水岭分割和梯度提升回归树的后处理,实现高精度的建筑提取。结果表明,数据预处理、改进深度学习算法可提高建筑检测像素精度IOU(Intersection over Union)约1%。后处理能充分利用网络输出的概率信息,有效优化建筑边界,在网络预测结果的基础上提高建筑实例召回率10.5%。本文方案与原始的ICT-Net网络相比,提高建筑实例召回率22.9%。High-density urban cities contain numerous similar buildings positioned in close proximity.Building detection from high-spatialresolution remote sensing imagery in such scenes remains a challenge in computer vision and remote sensing urban applications.The integration of traditional segmentation algorithms and a novel neural network is an effective approach for such challenging settings.Inspired by the recent success of deep-learning-based edge detection,a new building detection method aiming at accurate boundaries is proposed.In accordance with the characteristics of buildings and their border,this study improves the network structure and integrates the network with bottom-up watershed segmentation to improve boundary precision and classification accuracy.First,two auxiliary labels,namely,the building boundary and parting line,are derived from the original dataset through data preprocessing.Second,the newly proposed building detection frame called ICT-Net is improved by modifying its structure and loss function in accordance with the two auxiliary labels to obtain the probability of three classes.Lastly,a post-process integrating watershed segmentation with gradient-boosted regression trees is employed to achieve high-accuracy building detection.Specifically,a probability feature map is generated by merging the probability of three classes.Watershed segmentation with building marker thresholds is applied to obtain building instances from the probability feature map.Then,the building probability of each building instance predicted by gradientboosted regression trees is used to select building instances,resulting in building detection results.Parameter selection is also implemented.The performance of the proposed method is validated on the INRIA dataset,which provides aerial orthorectified color imagery with a spatial resolution of 0.3 m and with corresponding ground truth labels for two semantic classes:building and not building.Experimental results suggest that data preprocessing and the application of boundary

关 键 词：遥感 建筑检测 深度网络 边界损失 分水岭分割 实例分割 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TU198[自动化与计算机技术—计算机科学与技术]