LORENCS, A., MEDNIEKS, I., SINICA-SINAVSKIS, J., PUKITIS M. Fusion of Multisensor Data Based on Different Multidimensional Distributions, Electronics and Electrical Engineering (Elektronika ir Elektrotechnika, Vol. 22, No.4, pp. 68-72, 2016.
The paper addresses problems related with classification of images obtained using different types of remote sensors. 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 sensors 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.