Urban change detection based on coherence and intensity characteristics of SAR imagery

In this paper, an unsupervised change-detection approach was proposed to detect new urban areas from multi-temporal SAR images. The novelty of the proposed approach is the joint use of coherence and intensity characteristics Of SAR imagery. The approach involves two main steps: (a) the extraction of difference feature containing information on changed areas, and (b) the unsupervised two-dimensional (2D) thresholding. First, two difference features based on the concepts of long-term coherence and backscattering temporal variability are extracted from a series of multi-temporal SAR images. Then, the resulting features that represent the INSAR signal temporal variability of changed areas ore merged, and a 2D thresholding technique based on the maximum 2D Renyi's entropy criterion is developed to obtain the change-detection results. The effectiveness of the proposed approach is confirmed with experimental results obtained from a set of six ERs-1/2 SLC SAR images acquired in Shanghai, China.