Multi-sensor data fusion for long range demining area reduction

Multisensor data fusion is getting more importance with the increasing number of available satellite sensors. The aim of data fusion is to take advantages of combining different types of data to improve accuracies. However features extracted from different sensors will often have different statistical properties, and therefore combining data in an efficient way is not a trivial task. This paper proposes a new algorithm for data fusion between classification maps separately derived by application of clustering algorithms to PolSAR and Multi-spectral datasets. The expected new output is a map where all the classes identified with each single dataset will be present. The pixels assigned to the same class with both datasets will be characterized by a higher likelihood to belong to that class. The application for which the data fusion has been developed is that of hazardous areas reduction in land mines clearing operation. At this purpose a demonstration site has been set up in Poland within the FP7 D-BOX project and the fusion framework tested on it with acquisition of remote sensing imagery.