In recent years, the development of high-resolution remote sensing image extends the visual field of the terrain features. Quickbird and other high-resolution remote sensing image can show more characteristics such as shape, spectral, texture and so on. Back Propagation neural network is widely used in remote sensing image classification in recent years, it is a self-adaptive dynamical system which is widely connected by large amount of neural units, and it bases on distributing store and parallel processing. It study by exercise and had the capacity of integrating the information, synthesis reasoning, and rapid overall processing capacity. It can solve the regular problem arise from remote sensing image processing, therefore, it is widely used in the application of remote sensing. This paper discusses the Back Propagation neural network method in order to improve the high resolution remote sensing image classification precision. By analyzing the principle and learning algorithms of Back Propagation neural network, we utilize the Quickbird imagery of Beijing with high resolution as experimental data and do the research of road and simple building roof, In this paper, the use of remote sensing image processing software Matlab, and then combined with Back Propagation neural network classifier for the high resolution remote sensing images of their pattern recognition.
