VEGETATION CLASSIFICATION OF MULTISPECTRAL REMOTELY SENSED DATA USING NEURAL NETWORK
应用神经网络和多谱段遥感数据对大屿山岛土地覆盖分类的研究

Neural Networks have been proposed as a means of classifying remotely sensed data. In this paper, we address a land cover classification problem using multi-spectral Landsat Thematic Mapper(TM) data employing ANN. We design a MLP (Multi Layer Perceptron)Neural Network to classify the land cover type and compare the result with the conventional classification schemes. The results show that the neural network is superior to some of the classical statistical methods.