Impact of a spatial decorrelation of the noise on the estimation accuracy of temporal changes in the scene from a couple of single-look SAR images

In this work, we make use of an unsupervised procedure for the spatial decorrelation of fully-developed speckle. The goal is evaluating the impact of a preliminary spatial decorrelation of the noise on the accuracy of temporal change detection between two single-look images of the same scene taken at different times. In a likely simulated scenario, we optionally introduce a spatial correlation of the noise in the synthetic complex data by means of a 2D separable Hamming window in the Fourier domain. Then, we remove such a correlation by using the whitening procedure, take the modulus of the SLC images, apply change detection algorithms suitable for detected data, and compare the geometric and radiometric accuracy of the estimated change maps for the three following cases: uncorrelated noise, correlated noise, and decorrelated noise. Several change detection methods are considered: from the simple Log-Ratio operator preceded by despeckling, to more advanced parametric or nonparametric methods based on the Kullback-Leibler divergence or on the mean-shift clustering of the bivariate scatterplot. Simulation results show a consistent improvement of performance, notably the geometric accuracy of changes, but also their global extent.