Compound and configurable framework for exploratory earth observation data analysis

Abstract

The lack of a comprehensive solution for image information mining has often brought confusion and misunderstanding when Earth Observation data based application scenarios were addressed. Considering the variety of dedicated sensors available nowadays, the particularities of the recorded data raises serious issues when explored. Most of the proposed methodologies for data analysis integrate algorithms able to cope with single cases. In order to overcome this limitation, the present paper introduce a compound, configurable framework containing two processing levels, for feature extraction and image classification, that allows different settings depending on the application being handled. The design was proposed such that it facilitates the integration of several methods and algorithm for each level, including a module to serve for validation when reference data is available. The approach is not complete without the interaction with the user, therefore, a human-machine communication strategy was also developed. The validation was performed through a prototype system meeting all the criteria of the defined framework.