Applying neural networks in rare vegetation communities classification of remotely sensed images

Abstract

Artificial neural networks (ANNs) are used for rare vegetation communities’ classification using remotely sensed data. Training of a neural network requires that the user specifies the network structure and sets the learning parameters. Heuristics proposed by a number of researchers to determine the optimum values of network parameters are compared using datasets. Training and test samples were collected for each class type (12 classes). After preliminary statistical tests for training samples, two modification algorithms of the classification scheme were defined: the first one led to creating a scheme which consisted of 7 classes, and the second one led us to creating of 5 class’s scheme. Testing results show that the use of ANNs on the based of 5 class’s scheme can produce higher classification accuracies than either alternative. The visual analysis of the results of the classification is described using Geoinformation Technologies in details.