| Abstract [eng] |
Image analysis is very important because of its usage in many different areas of science and industry. Pattern recognition (classification) is a tool used in image analysis. Statistical pattern recognition, based on Bayes discriminant functions is the object of this work. The main problem is to classify stationary Gaussian random field observation into one off two classes, considering, that it is dependant on training sample ant taking in to account the relationship with training sample. The new supervised classification method, based on Bayes discriminant functions, is proposed and it gives better results comparing with other commonly used Bayes discriminant functions. Method is programmed with R program and investigated experimentally, reconstructing images corrupted by spatially correlated noise. Such situation occurs naturally, for example, during the forest fire smoke covers the remotely sensed image, gathered from the satellite. Also such situation is often during cloudy days. During such situation the incorporation of the spatial dependences into the classification problem is useful. Analytical error rates of Bayes discriminant functions are presented (derived), which are the criterion of these functions. Also, the dependences on statistical parameters are investigated for these error rates. |
