Abstract
A neural network was applied to land cover classification for the purpose of utilizing spatial information. Co-occurrence matrices, which are extracted from single band image data, were used for the input feature matrix to the neural network. The adopted model of the neural network has multi layered architecture with the back propagation algorithm as the training method. Firstly, various models and parameters were evaluated by classification experiments which were threshold models of each neuron, learning parameters at back propagation, the number of hidden layers, the number of neurons in the hidden layers and the size of co-occurrence matrix. Through the experiments, the best performance in landcover classification was achieved with sigmoid function for the threshold model, large learning rate and small momentum as the learning parameters, single hidden layer, two times of output neurons as the number of the hidden neurons and 16×16 to 32×32 as the size of co occurrence matrix. Secondly, landcover classification using the proposed method and three kinds of conventional methods were conducted with Landsat TM and SPOT HRV images. The conventional methods were a pixel wise maximum likelihood classifier, a pixel wise neural network classifier and a 3× 3 pixel wise neural network classifier. As a result, the proposed neural network classifier with the aid of co-occurrence matrix showed 5% to 17% higher classification accuracies than those of the conventional classifiers.
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