Advanced methods for change detection in VHR multitemporal SAR images

Change detection aims at identifying possible changes in the state of an object or phenomenon by jointly observing data acquired at different times over the same geographical area. In this context, the repetitive coverage and high quality of remotely sensed images acquired by Earth-orbiting satellites make such kind of data an ideal information source for change detection. Among the different kinds of Earth-observation systems, here we focus on Synthetic Aperture Radar (SAR). Differently from optical sensors, SAR is able to regularly monitor the Earth surface independently from the presence of cloud cover or sunlight illumination, making SAR data very attractive from an operational point of view. A new generation of SAR systems such as TerraSAR-X, TANDEM-X and COSMO-SkyMed, which are able to acquired data with a Very High geometrical Resolution (VHR), has opened new attractive opportunities to study dynamic phenomena that occur on the Earth surface. Nevertheless, the high amount of geometrical details has brought several challenging issues related to the data analysis that should be addressed. Indeed, even though in the literature several techniques have been developed for the automatic analysis of multitemporal low- and medium-resolution SAR data, they are poorly effective when dealing with VHR images. In detail, in this thesis we aim at developing advanced methods for change detection that are able to properly exploit the characteristics of VHR SAR images. i) An approach to building change detection. The approach is based on a novel theoretical model of backscattering that describes the appearance of new or fully collapsed buildings. The use of a fuzzy rule set allows in real scenarios an efficient and effective detection of new/collapsed building among several other sources of changes. ii) A change detection approach for the identification of damages in urban areas after catastrophic events such as earthquakes or tsunami. The approach is based on two steps: first the most damaged urban areas over a large territory are detected by analyzing high resolution stripmap SAR images. These areas drive the acquisition of new VHR spotlight images, which are used in the second step of the approach to accurately identify collapsed buildings. iii) An approach for surveillance applications. The proposed strategy detects the changes of interest over important sites such as ports and airports by performing a hierarchical multiscale analysis of the multitemporal SAR images based on a Wavelet decomposi- tion technique. iv) An approach to multitemporal primitive detection. The approach, based on the Bayesian rule for compound classification integrated in a fuzzy inference system, takes advantage of the multitemporal correlation of images pairs in order to both improve the detection of the primitives and identify the changes in their state. For each of the above mentioned topic an analysis of the state of the art is carried out, the limitations of existing methods are pointed out and the proposed solutions to the considered problems are described in details. Experimental results conducted on simulated and real remote sensing data are provided in order to show and confirm the validity of each of the proposed methods.