An open source object-based framework to extract landform classes

This paper introduces a new open source, knowledge-based framework for automatic interpretation of remote sensing images, called InterIMAGE. This framework exhibits a flexible modular architecture, in which image processing operators can be associated to both root and leaf nodes of a semantic network, which accounts for a differential strategy in comparison to other object-based image analysis platforms currently available. The architecture, main features as well as an overview on the interpretation strategy implemented in InterIMAGE are presented. The paper also reports an experiment on the classification of landforms. Different geomorphometric and textural attributes obtained from ASTER/Terra images were combined with fuzzy logic to drive the interpretation semantic network. Object-based statistical agreement indices, estimated from a comparison between the classified scene and a reference map, were used to assess the classification accuracy. The InterIMAGE interpretation strategy yielded a classification result with strong agreement and proved to be effective for the extraction of landforms.

► This paper describes an innovative system - InterIMAGE - for interpreting remotely sensed digital scenes based on cognitive approaches.
► In the practical experiment undertaken with this system, data obtained from an orbital optical sensor of medium spatial resolution drove a hierarchical semantic network, meant for the storage of the Interpreter’s knowledge. The system differential lies on its flexibility for embedding customized image processing routines (for both segmentation and classification) independently in each node of the semantic network.
► Our approach has been successfully applied to a geomorphological interpretation analysis, validated by statistical indices derived from an error matrix.