Abstract

The increasing availability of new generation remote sensing satellite multispectral images provides an unprecedented source of information for Earth observation and monitoring. Multispectral images can be now collected at high resolution covering (almost) all land surfaces with extremely short revisit time (up to a few days), making it possible the mapping of global changes. Extracting useful information from such huge amount of data requires a systematic use of automatic techiques in almost all applicative contexts. In some cases, the strict application requirements force the pratictioner to develop strongly data-driven approaches in the development of the processing chain. As a consequence, the exact relationship between the theoretical models adopted and the physical meaning of the solutions is sometimes hidden in the data analysis techniques, or not clear at all. Altough this is not a limitation for the success of the application itself, it makes however dicult to transfer the knowledge learned from one specic problem to another. In this thesis we mainly focus on this aspect and we propose a general mathematical framework for the representation and analysis of multispectral images. The proposed models are then used in the applicative context of change detection. Here, the generality of the proposed models allows us to both: (1) provide a mathematical explanation of already existing methodologies for change detection, and (2) extend them to more general cases for addressing problems of increasing complexity. Typical spatial/spectral properties of last generation multispectral images emphasize the need of having more exible models to image representation. In fact, classical methods to change detection that have worked well on previous generations of multispectral images provide sub-optimal results due to their poor capability of modeling all the complex spectral/spatial detail available in last generation products. The theoretical models presented in this thesis are aimed at giving more degrees of freedom in the representation of the images. The eectiveness of the proposed novel approaches and related techniques is demonstrated on several experiments involving both synthetic datasets and real multispectral images. Here, the improved flexibility of the models adopted allows for a better representation of the data and is always followed by a substantial improvement of the change detection performance.