Compression Based Pattern Recognition: Data Content Communication Channels

Abstract

The implicit data models and the expected parameters on which they are dependent may introduce biases in the Earth Observation (EO) data analysis methodologies. The estimated parameters can describe only particular, limited observations behavior. The broad diversity of the EO data, as sensing modalities, spatial, spectral, and radiometric resolution, and also the huge variety of the observed scenes make problematic the definition of a general model. The lecture presents a communication channel approach for image information extraction. The information retrieval is elaborated based on data compression methods independent of the type, form, content or purpose of the data. This paradigm is common to any data type without the weakening effect of specializing it for specific, particular applications fields. This is realized by approaching these challenges from an Information Theoretic perspective and using also the latest progress in Algorithmic Information Theory. The objective is re-formulating the definition of the “relevant information” in relation to the notions of “image content” and “context”, for a broader class of data, including scientific and engineering instruments records.