No abstract

This paper describes training of Multilayer Perceptron Neural classifier to extract rare vegetation objects from high spatial resolution IKONOS satellite imagery. There have been considered three options of training of the Multilayer Perceptron Neural according to three different classification schemes. At first 12 type of rare vegetation community types were defined, a main classification scheme (“Initial classification scheme”) was designed on that base. After prelim statistical tests on training samples two modification algorithms of the classification scheme were defined: the first one led to creating of scheme consisting of 7 classes (“Modified classification scheme”) and second one led us to creating of 5-classes scheme (“Optimized classification scheme“). The learning procedures of these classifiers are described as well as analysis and post processing of extraction results of objects of interest using Geoinformation Technologies in details.