Assessment of Earth Observation data content based on data compression - application to settlements understanding

Urban areas around the world are rapidly changing in an unregulated manner and remote sensing is the most effective option for their monitoring and planning. Good modeling of urban areas means reliable translation of the scene semantics into an algorithmic language. The compression based image retrieval techniques are data driven. The intention of employing compression based image retrieval techniques is to exploit the compression properties of the objects and estimate the shared information between them. Fast compression distance (FCD) is the similarity metric used in a compression based image retrieval technique that can be applied on large datasets. FCD between any two objects can be computed using the sizes of their dictionaries (sequence of recurring patterns) extracted through compression with LZW algorithm and the intersection of their dictionaries. In this paper, it is proposed to assess high resolution Earth Observation data content based on data compression for understanding urban settlements.