Change detection in repeat-pass multilook SAR imagery via coherence analysis

This work presents applications of an unsupervised method capable to provide estimates of temporal coherence starting from a couple of multilook detected SAR images of the same scene taken at different times. The method relies on robust measurements of the temporal correlation of speckle patterns occurring between the two pass dates. In the case of amplitude images the temporal correlation coefficient (TCC) of speckles was found to be a coherence estimator. A nonlinear transformation aimed at decor-relating the data across time while retaining the multiplicative noise model is defined as the pixel geometric men and ratio of two overlapped observations. The speckle TCC is estimated on small image blocks starting from the noise variances of a transformed couple of images. Experiments carried out on four repeat-pass observation from ERS-1 and on two SIR-C images taken in consecutive days, show that seasonal changes are accompanied by an abrupt decrement in TCC, while two temporally close observations exhibit TCC values somewhat larger, especially on urban areas, as it would be expected from a canonical coherence analysis.