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Global-Local-Aware conditional random fields based building extraction for high spatial resolution remote sensing images

  • role： First author第一作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

    National Engineering Research Center for Geographic Information System, China University of Geosciences,Wuhan 430078, China

  • Email：zhuqq@cug.edu.cn
  • Introduction：朱祺琪，1993年生，女， 副教授， 研究方向为多源遥感大数据提取分析及地学应用。E-mail： zhuqq@cug.edu.cn
ZHU Qiqi12,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

LI Zhen1,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

ZHANG Ya’nan1,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

LI Jialun1,  
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

DU Yuqiang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

    National Engineering Research Center for Geographic Information System, China University of Geosciences,Wuhan 430078, China

  • Email：guanqf@cug.edu.cn
  • Introduction：关庆锋，1977年生，男， 教授， 研究方向为时空大数据分析、挖掘及空间感知与计算。E-mail： guanqf@cug.edu.cn
GUAN Qingfeng12*,  
  • Affiliation：

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University,Wuhan 430071, China

LI Deren3

resumen

Obtaining building distribution maps accurately and quickly has very important research value. For instance, it can help governments and non-governmental organizations plan urban infrastructure construction and assist disaster relief work. The Conditional Random Field (CRF) is widely used in building extraction tasks because of its flexible context information modeling and detail extraction capabilities. However, problems, such as blurry building boundaries, still exist when the CRF is used to extract buildings from high spatial resolution remote sensing images.This study proposed a Global-Local-Aware conditional random fields framework for building extraction. Global-Local-Aware D-LinkNet (GLD-LinkNet) is proposed to solve the boundary blur problem of unary potential in this framework. GLD-LinkNet makes up for the loss of local structural information by D-LinkNet while using multi-scale building information effectively. In addition, the segmentation priors are fused, and the label cost is introduced. The pairwise potential reflects the linear combination of the spatial relationship of neighboring pixels and the cost of the local class label. It can maintain the detailed information inside the buildings effectively. In addition, to solve the problem of spectral similarity between buildings and noise, a larger range of context information is used to fuse segmentation prior to the extraction of buildings. The framework can eliminate the influence of image noise and spectral diversity within class. Moreover, it can also keep the detailed information of ground objects and determine the clear boundaries of buildings.Experiments were carried out on the aerial building dataset and the satellite building dataset of the WHU building datasets. Experimental results demonstrate that the proposed dilated and segmented conditional random field framework is superior to the state-of-the art methods in terms of accuracy and IOU. The model proposed in this study, which performs well for building extraction of complex scenes, can maintain the detailed information of buildings effectively. In addition, it removes the small building blocks mistakenly extracted by D-LinkNet, and the problem of blurred building boundaries has also been improved effectively.The use of the global and local integrated D-LinkNet to model the unary potential of CRF can realize the effective combination of building features of different scales. It makes the structure of the obtained buildings more complete. Furthermore, by adding a segmentation prior to the construction of pairwise potential, a building classification map with a clean background can be obtained. The introduction of the local class label cost term also meets the high requirements of the building extraction task for the extraction of building detail information and can capture detailed information that is difficult to identify on the network. The proposed model was tested on aerial and satellite datasets, and the IoU indicators on the two datasets reach 91.72% and 89.82%, respectively. These values imply that the framework can adapt to aerial and satellite datasets.In the future, we will further study the application of large-scale high-resolution remote sensing images in building extraction and try to combine multi-source geographic information data to extract more complete building information.

palabra clave

high spatial resolution remote sensing image;building extraction;conditional random fields;GLD-Linknet;the class label cost

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