Unsupervised Multitemporal Building Change Detection Framework Based on Cosegmentation Using Time-Series SAR

被引:16
|
作者
Zhang, Kaiyu [1 ,2 ,3 ]
Fu, Xikai [1 ,2 ]
Lv, Xiaolei [1 ,2 ,3 ]
Yuan, Jili [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Key Lab Technol Geospatial Informat Proc & Applic, Beijing 100190, Peoples R China
[2] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[3] Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China
关键词
cosegmentation; multitemporal change detection; object-based change detection (OBCD); SAR image;
D O I
10.3390/rs13030471
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Building change detection using remote sensing images is essential for various applications such as urban management and marketing planning. However, most change detection approaches can only detect the intensity or type of change. The aim of this study is to dig for more change information from time-series synthetic aperture radar (SAR) images, such as the change frequency and the change moments. This paper proposes a novel multitemporal building change detection framework that can generate change frequency map (CFM) and change moment maps (CMMs) from multitemporal SAR images. We first give definitions of CFM and CMMs. Then we generate change feature using four proposed generators. After that, a new cosegmentation method combining raw images and change feature is proposed to divide time-series images into changed and unchanged areas separately. Secondly, the proposed cosegmentation and the morphological building index (MBI) are combined to extract changed building objects. Then, the logical conjunction between the cosegmentation results and the binarized MBI is performed to recognize every moment of change. In the post-processing step, we use fragment removal to increase accuracy. Finally, we propose a novel accuracy assessment index for CFM. We call this index average change difference (ACD). Compared to the traditional multitemporal change detection methods, our method outperforms other approaches in terms of both qualitative results and quantitative indices of ACD using two TerraSAR-X datasets. The experiments show that the proposed method is effective in generating CFM and CMMs.
引用
收藏
页码:1 / 22
页数:22
相关论文
共 50 条
  • [1] Change Detection in Image Time-Series Using Unsupervised LSTM
    Saha, Sudipan
    Bovolo, Francesca
    Bruzzone, Lorenzo
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [2] Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models
    Jiang Liming
    Liao Mingsheng
    Zhang Lu
    Lin Hui
    GEO-SPATIAL INFORMATION SCIENCE, 2007, 10 (02) : 111 - 116
  • [3] Unsupervised Change Detection in Multitemporal SAR Images Using MRF Models
    JIANG Liming LIAO Mingsheng ZHANG Lu LIN Hui JIANG Liming
    Geo-Spatial Information Science, 2007, (02) : 111 - 116
  • [4] Change Detection Based on the Coefficient of Variation in SAR Time-Series of Urban Areas
    Koeniguer, Elise Colin
    Nicolas, Jean-Marie
    REMOTE SENSING, 2020, 12 (13)
  • [5] An approach to unsupervised change detection in multitemporal SAR images based on the generalized Gaussian distribution
    Bazi, Y
    Bruzzone, L
    Melgani, F
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 1402 - 1405
  • [6] An unsupervised approach based on geometrical structures to automatic change detection in multitemporal SAR images
    Chang, Bao
    Zhang, Gong
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2011, 39 (09): : 2125 - 2129
  • [7] Detecting Ephemeral Objects in SAR Time-Series Using Frozen Background-Based Change Detection
    Taillade, Thibault
    Thirion-Lefevre, Laetitia
    Guinvarc'h, Regis
    REMOTE SENSING, 2020, 12 (11)
  • [8] Detection of glaciers displacement time-series using SAR
    Euillades, Leonardo D.
    Euillades, Pablo A.
    Riveros, Natalia C.
    Masiokas, Mariano H.
    Ruiz, Lucas
    Pitte, Pierre
    Elefante, Stefano
    Casu, Francesco
    Balbarani, Sebastian
    REMOTE SENSING OF ENVIRONMENT, 2016, 184 : 188 - 198
  • [9] Change detection matrix for multitemporal filtering and change analysis of SAR and PolSAR image time series
    Thu Trang Le
    Atto, Abdourrahmane M.
    Trouve, Emmanuel
    Solikhin, Akhmad
    Pinel, Virginie
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2015, 107 : 64 - 76
  • [10] FluxEV: A Fast and Effective Unsupervised Framework for Time-Series Anomaly Detection
    Li, Jia
    Di, Shimin
    Shen, Yanyan
    Chen, Lei
    WSDM '21: PROCEEDINGS OF THE 14TH ACM INTERNATIONAL CONFERENCE ON WEB SEARCH AND DATA MINING, 2021, : 824 - 832