Studies On Classification Method Of Remote Sensing Image Based On Artificial Neural Network Model

Remote sensing(RS)image classification is always a pivotal part of remote sensing study. How to improve the accuracy of RS interpretation is a urgent problem in RS application. The automatic classification of traditional RS image is principal used in decision-making theory (or statistical) methods to classify. However, the existence of the phenomenon about RS images of low spatial resolution is appeared as well as "synonyms spectrum", "foreign body spectrum with", more often at fault, leakage points, which induce the low classification accuracy .The artificial neural network (ANN) has high degree of parallel processing, adaptive capacity, non-linear mapping capabilities, generalization ability. So artificial neural networks in remote RS in classifications applied research provides a new approach.Traditional RS classification is divided into supervised classification and unsupervised classification. At first, the paper briefly introduced several commonly used classification of supervised and unsupervised methods. Then we introduce the ANN structure and learning rules , as well as typical neural network models in remote sensing image classifications. The BP neural network model of remote sensing image classification is analysised detailed, and the basic algorithm for BP is optimized.Lastly,self-organizing feature neural network(SOFM) in remote sensing image classification is emphasized .In this paper,the power function convergence is used to solve the slow rate of SOFM learning algorithm convergence.Based on MATLAB as a platform, we build BP neural network and improved SOFM neural network, making Haidian District, Beijing remote sensing imaging as experiments object. We analysis and comparison the experiments results. The experiment shows that the improvement of SOFM neural network classification accuracy is higher than the traditional classification of RS image and BP neural network classification accuracy. So the improved SOFM neural network model of RS image classification is valid.