Automatic registration of a single SAR image and GIS building footprints in a large-scale urban area

被引:21
|
作者
Sun, Yao [1 ]
Montazeri, Sina [1 ]
Wang, Yuanyuan [1 ,2 ]
Zhu, Xiao Xiang [1 ,2 ]
机构
[1] German Aerosp Ctr DLR, Remote Sensing Technol Inst, Munchener Str 20, D-82234 Wessling, Germany
[2] Tech Univ Munich, Signal Proc Earth Observat, Arcisstr 21, D-80333 Munich, Germany
基金
欧洲研究理事会;
关键词
GIS building footprints; Large-scale; Registration; SAR image; Urban area; INFRASTRUCTURE; TOMOGRAPHY;
D O I
10.1016/j.isprsjprs.2020.09.016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Existing techniques of 3-D reconstruction of buildings from SAR images are mostly based on multibaseline SAR interferometry, such as PSI and SAR tomography (TomoSAR). However, these techniques require tens of images for a reliable reconstruction, which limits the application in various scenarios, such as emergency response. Therefore, alternatives that use a single SAR image and the building footprints from GIS data show their great potential in 3-D reconstruction. The combination of GIS data and SAR images requires a precise registration, which is challenging due to the unknown terrain height, and the difficulty in finding and extracting the correspondence. In this paper, we propose a framework to automatically register GIS building footprints to a SAR image by exploiting the features representing the intersection of ground and visible building facades, specifically the near-range boundaries in the building polygons, and the double bounce lines in the SAR image. Based on those features, the two data sets are registered progressively in multiple resolutions, allowing the algorithm to cope with variations in the local terrain. The proposed framework was tested in Berlin using one TerraSAR-X High Resolution SpotLight image and GIS building footprints of the area. Comparing to the ground truth, the proposed algorithm reduced the average distance error from 5.91 m before the registration to -0.08 m, and the standard deviation from 2.77 m to 1.12 m. Such accuracy, better than half of the typical urban floor height (3 m), is significant for precise building height reconstruction on a large scale. The proposed registration framework has great potential in assisting SAR image interpretation in typical urban areas and building model reconstruction from SAR images.
引用
收藏
页码:1 / 14
页数:14
相关论文
共 50 条
  • [21] EFFICIENT REGISTRATION FOR INSAR LARGE-SCALE IMAGE USING QUADTREE SEGMENTATION
    Wei, Shunjun
    Pu, Liming
    Tang, Xinxin
    Zhang, Xiaoling
    Shi, Jun
    IGARSS 2018 - 2018 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2018, : 4435 - 4438
  • [22] An Improved Optical Flow Method for Image Registration with Large-scale Movements
    XIONG JingYi LUO YuPin TANG GuangRong Department of AutomationTsinghua UniversityBeijing PRChina
    自动化学报, 2008, (07) : 760 - 764
  • [23] A SYSTEM FOR LARGE-SCALE IMAGE MAPPING AND GIS DATA-COLLECTION
    PRIES, RA
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 1995, 61 (05): : 503 - 511
  • [24] Large-scale building height retrieval from single SAR imagery based on bounding box regression networks
    Sun, Yao
    Mou, Lichao
    Wang, Yuanyuan
    Montazeri, Sina
    Zhu, Xiao Xiang
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2022, 184 : 79 - 95
  • [25] Building integrated large-scale urban infrastructures: Singapore's experience
    Lui, PC
    Tan, TS
    JOURNAL OF URBAN TECHNOLOGY, 2001, 8 (01) : 49 - 68
  • [26] A New Large-scale Image Automatic Annotation System based on WordNet
    Lu, Jianjiang
    Lu, Zining
    Li, Yang
    Zhao, Tianzhong
    Zhang, Yafei
    2008 INTERNATIONAL WORKSHOP ON EDUCATION TECHNOLOGY AND TRAINING AND 2008 INTERNATIONAL WORKSHOP ON GEOSCIENCE AND REMOTE SENSING, VOL 1, PROCEEDINGS, 2009, : 758 - 762
  • [27] Image Semantic Distance Metric Learning Approach for Large-scale Automatic Image Annotation
    Jin, Cong
    Jin, Shu-Wei
    IOTBD: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND BIG DATA, 2016, : 277 - 283
  • [28] An automatic image-text alignment method for large-scale web image retrieval
    Zhang, Baopeng
    Qu, Yanyun
    Peng, Jinye
    Fan, Jianping
    MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 76 (20) : 21401 - 21421
  • [29] An automatic image-text alignment method for large-scale web image retrieval
    Baopeng Zhang
    Yanyun Qu
    Jinye Peng
    Jianping Fan
    Multimedia Tools and Applications, 2017, 76 : 21401 - 21421
  • [30] Automatic image-text alignment for large-scale web image indexing and retrieval
    Zhou, Ning
    Fan, Jianping
    PATTERN RECOGNITION, 2015, 48 (01) : 205 - 219