Compression of multitemporal remote sensing images through Bayesian segmentation

Multitemporal remote sensing images are useful tools for many applications in natural resource management. Compression of this kind of data is an issue of interest, yet, only a few paper address it specifically, while general-purpose compression algorithms are not well suited to the problem, as they do not exploit the strong correlation among images of a multitemporal set of data. Here we propose a coding architecture for multitemporal images, which takes advantage of segmentation in order to compress data. Segmentation subdivides images into homogeneous regions, which can be efficiently and independently encoded. Moreover this architecture provides the user with a great flexibility in transmitting and retrieving only data of interest.