The Study Of Classification Method Of Remote Sensing Image Features Based On The BP Neural Network

Remote sensing technology is new scientific detection technology. It is a realization of the goal of long-range, non-contact measurement, target acquisition and analysis of a technology. Along with the computer and the development of space technology, all kinds of resources and environment the satellite launch and successful operation, through satellite remote sensing the height of the earth from space to panorama and the dynamic changes of various resources such as surface information extraction technology obtained fast development. Therefore, the remote sensing image recognition processing, namely through the extraction of image feature information, and use these features for image classification, to achieve image recognition has been one of the important problems to solve.Neural network for special organization learning and strong from the fault tolerance, and to solve nonlinear mapping problems in liquor unique function.Neural network than traditional statistical parameters classification method borrows a lot of advantage, still can have certain anti-noise ability, by widespread application in pattern recognition and image processing, and etc. According to have used in remote sensing image processing of neural network system, this paper on the application of the most extensive BP neural network are introduced in this paper. Due to the standard BP algorithm in the slow rate of convergence and the dependence of the initial parameters of the network image classification, it is easy to fall into the local minimum value problems. According to the weaknesses of the BP algorithm, we use the algorithm of the global search ability and strong hereditary genetic to train the BP initial network parameters, not only can Get the optimal chromosomes and improve classification efficiency, but also can solve the local minimum problem. Therefore this paper constructs a method, for remote sensing which is based on GA-BP three-layer neural network.Generally used when dealing with remote sensing image classification is a classification method based on spectral characteristics, namely, gray feature for remote sensing image classification, using the single-feature classification accuracy is not high, so this paper is based on spectral and texture features integration of the classification. To the extraction of the spectrum characteristics, using the method of extracting average gray feature. Also use the same method for texture feature extraction, first calculate the GLCM of pixels in the window, and then calculate the average correlation characteristics and energy characteristic,and using three characteristics to represent the characteristic information of the image pixels within the entire window. After that, we make these three eigenvalues be normalized as the GA-BP network data input; and we classify and evaluate the image which make use of gray feature value and integration of the characteristic value as the input. The results show that using the improved GA-BP network integration of the eigenvalue of the network input, the classification accuracy and classification speed is higher than the classification results of single-feature. Figure24table15reference44...