ANN Remote Sensing Classification Model and Its Integration Approach with Geo-knowledge
人工神经网络遥感影像分类模型及其与知识集成方法研究

The Classification of remotely sensed data is the main theme of Remote Sensing Image Understanding and Analysis, while the artificial neural networks (ANN) is one of the latest and most important techniques developed recently in the area of connective artificial intelligence. In this paper, after we made a through study on the structure of the multi_layer perceptron (MLP) and deeply analyzed it's back_propagation (BP) training algorithm, the framework of how to integrate Geo_knowledge with ANN and apply to RS classification is put forward. Firstly, the suggestions of improving the efficiency of BP algorithm, including network architecture selection, use of optimization on learning rate, and assistance with additional data and expert knowledge etc., are presented. Then, after the general approach of ANN based RS image classification is reviewed, the model of integrating Geo_knowledge with ANN for RS image classification is developed with specific experiment of RULE based MLP. Experimental result shows significant improvement in comparison with statistical and traditional ANN classifiers.