Big data classification of remote sensing image based on cloud computing and convolutional neural network

Abstract

In recent years, with the continuous development of cloud computing and big data technology, information technology is penetrating all corners of enterprise development at a speed that ordinary people cannot imagine. Today's remote sensing image classification plays an important role in many applications. This article is based on cloud computing and convolutional neural network to study the remote sensing image big data classification. The article aims to fully focus on cloud computing technology and convolutional neural network technology to carry out specific application research, on this basis, to obtain a more convenient, accurate and efficient remote sensing image classification method, and to prove the feasibility of this method. This article takes cloud computing technology and convolutional neural network technology as the technical points. First, it introduces cloud computing technology in more detail from the basic concepts, characteristics and classification of cloud computing and then compares traditional methods from the selection of feature extraction methods and classifiers. The remote sensing image classification method is described, and finally, the convolutional neural network model that needs to be applied in this research is explained. In the experiment, the features extracted by multiple pre-training networks are fused. Through the study of the three feature fusion methods, it is found that the classification accuracy rate has been further improved on the three data sets. The experimental results in this paper show that the accuracy of the traditional method is below 80%, while the feature extraction of the pre-trained network has an accuracy of more than 90% on the UCMERCE data; it combines the three pre-trained networks of ResNet-50, ResNet-101 and ResNet-152, an increase of more than 2.5%.