Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images  

作　　者：FENG Hao WU Jianzhong ZHANG Lu LIAO Mingsheng 

机构地区：[1]State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China [2]Key Laboratory of Land Subsidence Monitoring and Prevention,Ministry of Land and Resources,Shanghai 200072,China [3]Shanghai Engineering Research Center of Land Subsidence,Shanghai 200072,China [4]Shanghai Institute of Geological Survey,Shanghai 200072,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第4期896-906,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(41774006);the Comparative Study of Geo-environment and Geohazards in the Yangtze River Delta and the Red River Delta Project;the Shanghai Science and Technology Development Foundation(20dz1201200)。

摘　　要：Constrained by complex imaging mechanism and extraordinary visual appearance,change detection with synthetic aperture radar(SAR)images has been a difficult research topic,especially in urban areas.Although existing studies have extended from bi-temporal data pair to multi-temporal datasets to derive more plentiful information,there are still two problems to be solved in practical applications.First,change indicators constructed from incoherent feature only cannot characterize the change objects accurately.Second,the results of pixel-level methods are usually presented in the form of the noisy binary map,making the spatial change not intuitive and the temporal change of a single pixel meaningless.In this study,we propose an unsupervised man-made objects change detection framework using both coherent and incoherent features derived from multi-temporal SAR images.The coefficients of variation in timeseries incoherent features and the man-made object index(MOI)defined with coherent features are first combined to identify the initial change pixels.Afterwards,an improved spatiotemporal clustering algorithm is developed based on density-based spatial clustering of applications with noise(DBSCAN)and dynamic time warping(DTW),which can transform the initial results into noiseless object-level patches,and take the cluster center as a representative of the man-made object to determine the change pattern of each patch.An experiment with a stack of 10 TerraSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential applicability to wide area change detection.

关 键 词：change detection multi-temporal synthetic aperture radar(SAR)data coherent and incoherent features CLUSTERING 

分 类 号：TN957.52[电子电信—信号与信息处理]