Optimizing SAR change detection based on log-ratio features

The paper investigates how to optimize the performances of unsupervised log-ratio based change detection algorithms for two-date 1-look amplitude SAR images. The usual approach of pre-processing the SAR images at different dates with state-of-the-art despeckling filters is critically discussed. Those adaptive filters are very efficient, also for the challenging case of 1-look images, for speckle reduction of single-date image data and then for providing reliable classification, detection, or parameter estimation results. However, they are not able to ease the discrimination of statistical from structural changes in 1-look SAR images for which reliable point-target detection is nearly impractical. A simple, yet very effective, multiscale method for change detection and automatic change mapping is proposed and tested on simulated 1-look SAR images. The adopted pre-processing is based on guided image filtering with different window sizes. It improves the detection of changed regions without introducing any geometrical constraint and significantly reduces the false alarm rate. Experimental tests on simulated SAR images and Spotlight COSMO-SkyMed images demonstrate the advantages of the proposed algorithm.