Image information mining for coastal disaster management

In this paper we propose a framework that focuses on the need for rapid image information mining in a coastal disaster event where it is necessary to explore vast amounts of data from multiple remote sensing sensors in real or near real time. The proposed system; Rapid Image Information Mining (RIIM) is a region based approach where in lieu of prevalent pixel based methods it localizes interesting zones and extracts information from them that are stored in a knowledge base. A set of primitive features are extracted from the regions, whose relevance for a particular land cover class or a combination of classes is then assessed based on a wrapper based genetic algorithm (GA) approach. In this, we use an induction algorithm along with the GA to arrive at an optimal set of features. We investigate feature selection and feature generation using this wrapper approach. A support vector machines based classification is applied for generating predictive models for those land cover classes that are important in coastal disaster events. In RUM, searching for a particular land cover type (e.g. flooded agriculture) is based on the actual meaning and content of it in the image instead of just the metadata.