Fusion of Multisensor Data Based on Different Multidimensional Distributions

The paper addresses problems related to classification of images obtained by various types of remote sensing devices. Development and use of Bayes type land cover classifiers based on multidimensional Gaussian, Dirichlet and gamma distributions is analysed and compared on the basis of sample data from RGB and hyperspectral thermal sensing devices with unequal spatial resolution. Approaches to data fusion for design of the combined classifiers are presented including the cases where different families of multidimensional distributions are used to model the sensor data and classifiers are designed using combinations of their probability density functions. The best classification results are obtained when the fusion of data from both images is used together with classification based on all three considered distributions combined together.