The Application Of Artificial Neural Network Differentiating Kinds Of Terrain In Zhong-Shan Mausoleum

The traditional remote sensing image classification mthods are Maximum-likelihood, Min-distance classification,etc. In this paper, the artificial neural network(ANN) model was introduced to classify five typical kinds of terrain in Zhong-Shan Mausoleum, such as forest, water, farmland and grassland,building,and others. Based on the principle, the paper compared the distinctness of the three classification methods with the attempt to bring out the trend of remote sensing image classification being used in Zhong-Shan Mausoleum.On the basis of geometric correction for remote sensing images data, detailed character analysis was conducted for the TM images. Then several image transformations such as interactive contrast stretching , ratio enhancement, principal components transformation, tasseled-cap transformation, image color conbine, etc, were implemented.Two indexes was calculated to estimate the best bands union for color combination,one is Optimum Index Factor(OIF), the other is the determinant of the co-variance matrix. It can be seen from the result that for color combination the original optimal bands were TM2, TM4, TM7, the best mixed images were PC1, NDVI, TM4.The main drawback of traditional remote sensing image classification methods is its low precision. The neural networl-based remote sensing image classification technique has been presented. The result demonstrated that the neural network classification system could be used in remote sensing image classification, and its classification precision was superior to that, of the Maximum-likelihood and Min-distance, achieving an accuracy of 96.43%.In this paper,the author applied the gray-level values of three bands union as the network model's input variables, and chose five kinds terrain as the model's output variables. First the original Back Propagation Network was used to classify terrain types, the result of the classification demonstrated that Training RMS Error couldn't descend the expected value. Then Momentum mothed tried to improve the original Backpropagation algorithm, the experimental results show that network converged speedily achieving the expected value(0.1).Accuracy analysis is a necessary work in the classification of remote sensing data. In this paper, the evaluating indexes included overall accuracy, Kappa coefficient, product accuracy, user accuracy, etc. In a word, ANN classification is superior to traditional methods, and has wide prospects in forestry remote sensing.