【摘要】 从遥感图像中自动化地检测和提取建筑物在城市规划、人口估计、地形图制作和更新等应用中具有极为重要的意义。本文提出和展示了建筑物提取的数个研究进展。由于遥感成像机理、建筑物自身、背景环境的复杂性,传统的经验设计特征的方法一直未能实现自动化,建筑物提取成为30余年尚未解决的挑战。先进的深度学习方法带来新的机遇,但目前存在两个困境:①尚缺少高精度的建筑物数据库,而数据是深度学习必不可少的"燃料";②目前国际上的方法都采用像素级的语义分割,目标级、矢量级的提取工作亟待开展。针对于此,本文进行以下工作:①与目前同类数据集相比,建立了一套目前国际上范围最大、精度最高、涵盖多种样本形式(栅格、矢量)、多类数据源(航空、卫星)的建筑物数据库(WHU building dataset),并实现开源;②提出一种基于全卷积网络的建筑物语义分割方法,与当前国际上的最新算法相比达到了领先水平;③将建筑物提取的范围从像素级的语义分割推广至目标实例分割,实现以目标(建筑物)为对象的识别和提取。通过试验,验证了WHU数据库在国际上的领先性和本文方法的先进性。更多还原

【Abstract】 Automatic extraction of buildings from remote sensing images is significant to city planning, popular estimation, map making and updating.We report several important developments in building extraction. Automatic building recognition from remote sensing data has been a scientific challenge of more than 30 years. Traditional methods based on empirical feature design can hardly realize automation. Advanced deep learning based methods show prospects but have two limitations now. Firstly, large and accurate building datasets are lacking while such dataset is the necessary fuel for deep learning. Secondly, the current researches only concern building’s pixel wise semantic segmentation and the further extractions on instance-level and vector-level are urgently required. This paper proposes several solutions. First, we create a large, high-resolution, accurate and open-source building dataset, which consists of aerial and satellite images with both raster and vector labels. Second,we propose a novel structure based on fully neural network which achieved the best accuracy of semantic segmentation compared to most recent studies. Third, we propose a building instance segmentation method which expands the current studies of pixel-level segmentation to building-level segmentation. Experiments proved our dataset’s superiority in accuracy and multi-usage and our methods’ advancement. It is expected that our researches might push forward the challenging building extraction study.更多还原