Urban Change Detection Using Coherence and Intensity Characteristics of Multi-temporal ERS-1/2 Imagery

In this paper, the potential of multitemporal ERS-1/2 InSAR data for detecting urban land-cover changes is investigated on a test area within Shanghai city, China. A new unsupervised change-detection approach is proposed, which is characterized by the combined use of two temporal variations based on backscattering intensity and long-term coherence from multitemporal ERS-1/2 SAR images. The proposed approach is mainly made up of two steps: feature extraction and unsupervised 2-D (two dimensional) thresholding. In the first step, two features are based on the concepts of backscattering intensity variation and long-term coherence variation respectively, and have been defined according to the analysis of different signal behavior of interferometric SAR in the presence of land-cover classes in urban area. In the second step, an unsupervised 2-D thresholding technique based on maximum 2-D Renyi’s entropy criterion is proposed. The thresholding is performed on the two difference images derived from the two features to produce an accurate changedetection map with two classes: changed and no-changed. Experimental results obtained from a set of six multitemporal ERS-1/2 SAR images show the effective of the proposed approach, and that ERS-1/2 InSAR data could be exploited for detecting urban land-cover changes.