ASSESSING BUILDINGS DAMAGE FROM MULTI-TEMPORAL SAR IMAGES FUSION USING SEMANTIC CHANGE DETECTION

被引:3
|
作者
Pang, Lei [1 ,2 ,3 ]
Zhang, Fengli [1 ,2 ,3 ]
Li, Lu [1 ,2 ,3 ]
Huang, Qiqi [1 ,2 ,3 ]
Jiao, Yanan [1 ,2 ,3 ]
Shao, Yun [1 ,2 ,3 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China
[3] Deqing Acad Satellite Applicat, Lab Target Microwave Properties, Huzhou 313000, Peoples R China
基金
中国国家自然科学基金;
关键词
SAR; Buildings damage; Semantic change detection; Siamese network;
D O I
10.1109/IGARSS46834.2022.9884915
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
A prompt and accurate assessment of buildings' damage is critical for disaster management and emergency response. With the development of high-resolution synthetic aperture radar (SAR) and deep-learning methods, more efficient damage assessment techniques based on building-units are possible. This paper proposes a new building damage assessment method using high-resolution SAR images based on semantic change detection. It utilizes a Siamese-based module for damage change detection together with an attention mechanism-based module for semantic segmentation of the damage maps. To evaluate the proposed model, a new damage assessment dataset is constructed from the SAR imagery originated from the battle of Aleppo, Syria, for model training and testing. The experiments performed on this dataset show an overall accuracy of 88.3%. The proposed method effectively identifies the damaged areas of the buildings and grade the damage condition.
引用
收藏
页码:6292 / 6295
页数:4
相关论文
共 50 条
  • [1] Bayesian change detection for multi-temporal SAR images
    Coulon, M
    Tourneret, AY
    [J]. 2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 1285 - 1288
  • [2] Data fusion approach for change detection in multi-temporal ERS-SAR images
    Bujor, FT
    Valet, L
    Trouvé, E
    Mauris, G
    Classeau, N
    Rudant, JP
    [J]. IGARSS 2001: SCANNING THE PRESENT AND RESOLVING THE FUTURE, VOLS 1-7, PROCEEDINGS, 2001, : 2590 - 2592
  • [3] Study on Change Detection for Multi-temporal Polarimetric SAR Images
    Zhang Juntuan
    Huang Shiqi
    Li Zhenfu
    Lin Jun
    [J]. 2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2, 2008, : 1182 - +
  • [4] Feature detection in multi-temporal SAR images
    Bujor, FT
    Trouvé, E
    Valet, L
    Bolon, P
    Nicolas, JM
    Rudant, JP
    [J]. ANALYSIS OF MULTI-TEMPORAL REMOTE SENSING IMAGES, 2004, 3 : 30 - 38
  • [5] Multi-Temporal SAR Change Detection using Wavelet Transforms
    Bouhlel, Nizar
    Rousseau, David
    [J]. 2022 30TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO 2022), 2022, : 538 - 542
  • [6] Multi-temporal change detection for SAR imagery
    Oliver, C
    McConnell, I
    Corr, D
    [J]. SAR IMAGE ANALYSIS, MODELING, AND TECHNIQUES II, 1999, 3869 : 55 - 66
  • [7] MULTI-TEMPORAL SAR CHANGE DETECTION AND MONITORING
    Hachicha, S.
    Chaabane, F.
    [J]. XXII ISPRS CONGRESS, TECHNICAL COMMISSION VII, 2012, 39 (B7): : 293 - 298
  • [8] Information fusion techniques for change detection from multi-temporal remote sensing images
    Du, Peijun
    Liu, Sicong
    Xia, Junshi
    Zhao, Yindi
    [J]. INFORMATION FUSION, 2013, 14 (01) : 19 - 27
  • [9] A DECOMPOSITION MODEL FOR SCATTERERS CHANGE DETECTION IN MULTI-TEMPORAL SERIES OF SAR IMAGES
    Lobry, S.
    Tupin, F.
    Denis, L.
    [J]. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 3362 - 3365
  • [10] Application of log-cumulants to change detection in multi-temporal SAR images
    Bujor, FT
    Nicolas, JM
    Trouvé, E
    Rudant, JP
    [J]. IGARSS 2003: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS I - VII, PROCEEDINGS: LEARNING FROM EARTH'S SHAPES AND SIZES, 2003, : 1386 - 1388