Classification of aerial photograph using neural network

The purpose of this paper is to apply a neural network to classify aerial photographs. An adaptive backpropagation algorithm is introduced to build a four-layer neural network as the practical model to classify aerial photographs based on pixel-pixel. The practical example is the classification of lake, forest and land, comparing a neural network with the maximum likelihood classification through the analysis of the process of classification and the results. The four-layer neural network introduced in this paper succeeded in building the complex model as an aerial photograph and it solved the problem of the great storage of data. The adaptive back-propagation algorithm adopted in this paper speeded up the learning rate, the general error decreased to a smaller degree, and the generalization ability of the neural network was also improved. The results demonstrated that the neural network is suitable to be used in the classification of remotely sensed data and is superior to the maximum likelihood classifier in the accuracy of the classification and the overall effect.