Urban Building Change Detection in SAR Images Using Combined Differential Image and Residual U-Net Network

被引:60
|
作者
Li, Lu [1 ,2 ]
Wang, Chao [1 ,2 ]
Zhang, Hong [1 ]
Zhang, Bo [1 ]
Wu, Fan [1 ]
机构
[1] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
基金
中国国家自然科学基金;
关键词
weighted function; color difference image; urban building change detection; synthetic aperture radar (SAR); residual U-Net; UNSUPERVISED CHANGE DETECTION; AUTOMATIC CHANGE DETECTION; MODEL;
D O I
10.3390/rs11091091
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar images quickly with fine spatiotemporal resolution and are not affected by weather conditions, making multi-temporal SAR images suitable for change detection. In this paper, a new urban building change detection method based on an improved difference image and residual U-Net network is proposed. In order to overcome the intensity compression problem of the traditional log-ratio method, the spatial distance and intensity similarity are combined to generate a weighting function to obtain a weighted difference image. By fusing the weighted difference image and the bitemporal original images, the three-channel color difference image is generated for building change detection. Due to the complexity of urban environments and the small scale of building changes, the residual U-Net network is used instead of fixed statistical models and the construction and classifier of the network are modified to distinguish between different building changes. Three scenes of Sentinel-1 interferometric wide swath data are used to validate the proposed method. The experimental results and comparative analysis show that our proposed method is effective for urban building change detection and is superior to the original U-Net and SVM method.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Building Detection on Aerial Images Using U-NET Neural Networks
    Ivanovsky, Leonid
    Khryashchev, Vladimir
    Pavlov, Vladimir
    Ostrovskaya, Anna
    [J]. PROCEEDINGS OF THE 24TH CONFERENCE OF OPEN INNOVATIONS ASSOCIATION (FRUCT), 2019, : 116 - 122
  • [2] An Efficient Change Detection for Large SAR Images Based on Modified U-Net Framework
    Wei, Jujie
    Zhang, Yonghong
    Wu, Hong'an
    Cui, Bin
    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2020, 46 (03) : 272 - 294
  • [3] U-Net for Taiwan Shoreline Detection from SAR Images
    Chang, Lena
    Chen, Yi-Ting
    Wu, Meng-Che
    Alkhaleefah, Mohammad
    Chang, Yang-Lang
    [J]. REMOTE SENSING, 2022, 14 (20)
  • [4] Semantic Image Segmentation for Building Detection in Urban Area with Aerial Photograph Image using U-Net Models
    Irwansyah, Edy
    Heryadi, Yaya
    Gunawan, Alexander Agung Santoso
    [J]. 2020 IEEE ASIA-PACIFIC CONFERENCE ON GEOSCIENCE, ELECTRONICS AND REMOTE SENSING TECHNOLOGY (AGERS 2020): UNDERSTANDING THE INTERCTION OF LAND, OCEAN AND ATMOSPHERE: DISASTER MITIGATION AND REGIONAL RESILLIENCE, 2020, : 48 - 51
  • [5] Fault Detection on Seismic Structural Images Using a Nested Residual U-Net
    Gao, Kai
    Huang, Lianjie
    Zheng, Yingcai
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [6] Fault Detection on Seismic Structural Images Using a Nested Residual U-Net
    Gao, Kai
    Huang, Lianjie
    Zheng, Yingcai
    [J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60
  • [7] Building extraction from remote sensing images using deep residual U-Net
    Wang, Haiying
    Miao, Fang
    [J]. EUROPEAN JOURNAL OF REMOTE SENSING, 2022, 55 (01) : 71 - 85
  • [8] Urban Change Detection for High-resolution Satellite Images Using U-Net Based on SPADE
    Song, Changwoo
    Wahyu, Wiratama
    Jung, Jihun
    Hong, Seongjae
    Kim, Daehee
    Kang, Joohyung
    [J]. KOREAN JOURNAL OF REMOTE SENSING, 2020, 36 (06) : 1579 - 1590
  • [9] A Residual Dense U-Net Neural Network for Image Denoising
    Gurrola-Ramos, Javier
    Dalmau, Oscar
    Alarcon, Teresa E.
    [J]. IEEE ACCESS, 2021, 9 : 31742 - 31754
  • [10] URNet: A U-Net based residual network for image dehazing
    Feng, Ting
    Wang, Chuansheng
    Chen, Xinwei
    Fan, Haoyi
    Zeng, Kun
    Li, Zuoyong
    [J]. APPLIED SOFT COMPUTING, 2021, 102