Unsupervised change detection of flood affected areas in SAR images using Rayleigh-based Bayesian thresholding

In the Bayesian context, the rationale for using a particular probability density function (pdf) for a synthetic aperture radar (SAR) image improves the performance of unsupervised change detection (CD). So, this work is concentrated on promoting the consequence of selecting an empirical pdf and its parameter estimation technique. Rayleigh pdf is used to model approximation and detail sub-bands of a dual tree complex wavelet transform (DTCWT) of the log ratio difference image. The chosen pdf has a potential impact on impulsive SAR images. The scale parameter of Rayleigh pdf is estimated using first-order moments by exploiting Mellin transform. This iteration-free parameter estimation technique, made the method faster. In each sub-band at each level of DTCWT decomposition, the binary threshold is estimated to represent the change signal at different scales. The performance in the final change map is improved by combining intra-scale along with inter-scale sub-band coefficients. The proposed method is tested on three flooding SAR image data sets acquired from European Space Agency environmental satellite, European remote-sensing satellite (ERS)-2 and ERS-1 SAR instruments. The experimental results show that the proposed method achieves good CD performance in terms of false alarm rate and overall detection accuracy in less computing time.