The Research And Application Intelligent Interpretation Of Remote Sensing Images Technology Research And Application

In recent decades,especially since the 21 st century,on the one hand,with the rapid development of satellite technology,high-resolution satellite remote sensing images have been more and more widely used in various fields of military and civilian,and also put forward higher requirements for intelligent interpretation technology of remote sensing images;on the other hand,under the tide of artificial intelligence,artificial intelligence+ Satellite remote sensing has been studied more and more deeply.Under this background,taking high-resolution satellite remote sensing image as the research object,this paper deeply studies the intelligent interpretation technology such as classification and target recognition of satellite remote sensing image based on computer vision,and on this basis,compiles application software to improve the automation level of remote sensing image analysis in many ways.Firstly,this paper uses convolution neural network to extract and classify remote sensing image features.Remote sensing image classification is a key technology in remote sensing digital processing.This paper abandons the traditional feature-based coding method,mainly studies three strategies based on deep learning and convolution neural network,and analyses their advantages and disadvantages.The first strategy is to train a new convolutional neural network separately.This paper designs and adjusts the parameters of the network comprehensively,and proposes an effective data enhance ment method for multi-scale random clipping.The second strategy is to use the existing convolution neural network to extract the features of remote sensing images,and classify the extracted features directly.The third strategy is to transfer and learn the existing neural network and fine-tune the parameters to obtain a more suitable depth convolution neural network for remote sensing image database.This paper uses the method of multi-model fusion to solve the problem.The inaccuracy of individual classification.Then,the models obtained by these three strategies are compared and studied,and the corresponding conclusions are obtained,which lay the foundation for the follow-up study.Secondly,considering that satellite image does not have the characteristics of overall downward,higher resolution than natural image,higher object aggregation,uneven distribution,exaggerated aspect ratio and more small targets,this paper improves the satellite image based on Faster R-CNN,YOLO V3 and other algorithms,mainly including adaptive modification of RPN network,increase the output of rotating theta angle.In the module,the method of void convolution is introduced,and the target recognition technology of satellite remote sensing image is studied,and good results are achieved.By applying the improved algorithm to practical application,we can obtain more real-time,accurate and effective thematic data and information.Finally,the software design of remote sensing image intelligent parsing system under Linux,x86 and macOS environment is carried out,and the intelligent processing software of remote sensing image is developed.The software system mainly includes three parts: system tools,classification subsystem and target recognition subsystem,especially target recognition subsystem.The recognizable object categories reach 1.Five kinds of software have high recognition accuracy and can reach the level of enterprise application.Based on this,the code is reconstructed,the small program which can be used on the mobile terminal is compiled,and the software system is tested and analyzed.