Application Of BP Neural Network Algorithm In Satellite Remote Sensing Image Classification

The purpose of setting up nature reserves is to protect the unique natural ecological system and rare and endangered wildlife.With the corresponding ecological value and scientific research significance,according to the difference of protection level,there are usually three different zones: core zone,experimental zone and buffer zone.At present,in the process of nature reserve construction,it is essential to study the class ification of its features.The nature reserve has a large area,diverse topography and many types of land features,so it is usual ly monitored by remote sensing.With the maturity of satellite sensor technology and the improvement of remote sensing technology,the resolution of remote sensing image has exceeded the sub-meter level.How to use texture and spectral information of remote sensing images with certain resolution to various industries has become a very important issue in the field of remote sensing applications.There are some shortcomings in remote sensing visual interpretation,such as long time consuming,huge workload and strong subjectivity.With the maturity of artificial intelligence technology,the classification mechanism based on artificial neural network is gradually applied to remote se nsing classification.Compared with manual visual interpretation,it is more objective and accurate.In order to study the application of neural network algorithm in remote sensing image and terrain,this paper completes image and terrain classification on ENVI,a professional remote sensing information processing platform,based on the ori ginal image data of Friendship national nature reserve GF-1.Firstly,the original image data in the study area are preprocessed,including ortho-rectification,radiation calibration,data fusion,atmospheric correction and image clipping.Then the optimal band combination is used to extract the texture features of image spectral features and gray correlation as the basis for subsequent classification.Neural network algori thm belongs to a kind of mathematical model which simulates the operation of human brain based on bionics.It has strong learning ability and analytical ability.It can acquire and analyze the differences of spectral and texture features of different types of objects in images,and then automatically divide remote sensing images into different types of land species,which can greatly improve the efficiency and accuracy of classification of land features.Next,we select training samples,build a neural network model,train the structure of the neural network,and complete the two phases of image and terrain classification of Friendly national nature reserve.Finally,the accuracy of classification conclusion is evaluated by error matrix,and the distribution of various objects in various functional areas is explored.The experimental results show that the classification accuracy in 2014 is 94.1%,the Kappa coefficient is 0.901;the classification accuracy in 2017 is 94.25%,and the Kappa coefficient is 0.916.The research proves that the classification accuracy of the neural network algorithm is high when it is applied to remote sensing image classification,and it meets the requirements of the application of land feature classification.