Application And Contrast Research On Remote Sensing Image Classification Based On Artificial Neural Network

With remote sensing data spatial resolution, spectral resolution, the temporal resolution of constant improvement,now it has set a space massive data for multi-source, multi-spectral, multi-resolution remote sensing images. But how to use these data to tap the hidden information is the key issues to restrict the development and application of remote sensing. The image classification method as an important aspect in remote sensing extracting information,using the application of traditional Bayesian classifier,has been unable to meet the needs of accuracy, but artificial neural network classification methods based on non-linear mapping will provide more ideal solution for this issue,beacuse this classification method is not based on an assumptive probability distribution, but through training samples to learn network weights,and make up a classifier. A neural network algorithm for image classification, to a certain extent, can eliminate the ambiguity and uncertainty caused by traditional image classification.Take the TM remote sensing data in 2002 in Northeast Hohhot as data source, based on in-depth study of standard BP neural network theory, through choosing the training sample building methods and training algorithm, confirming the number of best hidden layer neurons,to system study on the processes of BP neural network classification and to show the improve methods for the problems that exist in the realization process. The results show: if used the training samples constructed with sample mean method,the precision high and the convergence rapid, but it can not be achieved effectively classification; the LM algorithm BP Neural networks ,which is found in the issues that standard BP network has slow convergence and is vulnerable to the smallest of local,greatly improve training speed and accuracy of convergence,and made a very good classification results; basis on the above, in order to overcome the high correlation between the band and data redundancy, will do the principal component analysis on TM1, TM2, TM3 of three band, take the first principal component, set a dataset with other band, and using this data to complete network training and study of classification. The results show that under the similar classification accuracy conditions, the principal component transformation can make more rapid convergence and faster simulation speed.Comparison the BP neural network classification results based on LM algorithm with the maximum likelihood classification and visual interpretative results, overall, the BP network method is better effects than the maximum likelihood, and closer visual interpretative results. BP neural network classification of remote sensing images is more improved than traditional classification methods .However, using neural network model still has many problems to be solved. For example, determine of the neural networks best structure; the selection of parameters;as neural network also classify on remote sensing images based on spectral characteristics,so it exists the same problem with the maximum likelihood method, does not solve the "different body with same spectrum," "same body with different spectrum", other data to be holp to address and to further improve classification accuracy to meet the practical application needs.