Constrained by complex imaging mechanism and extraordinary visual appearance, change detection with syn-thetic 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 frame-work using both coherent and incoherent features derived from multi-temporal SAR images. The coefficients of variation in time-series 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 spa-tial 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 Ter-raSAR-X images in Stripmap mode demonstrated that this method is effective in urban scenes and has the potential appli-cability to wide area change detection.

FENG Hao;WU Jianzhong;ZHANG Lu;LIAO Mingsheng

State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Key Laboratory of Land Subsidence Monitoring and Prevention,Ministry of Land and Resources,Shanghai 200072,China;Shanghai Engineering Research Center of Land Subsidence,Shanghai 200072,China;Shanghai Institute of Geological Survey,Shanghai 200072,China

系统工程与电子技术（英文版）

[1]FENG Hao;WU Jianzhong;ZHANG Lu;LIAO Mingsheng-.Unsupervised change detection of man-made objects using coherent and incoherent features of multi-temporal SAR images)[J].系统工程与电子技术（英文版）,2022(04):896-906

raSAR,Stripmap

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