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Remote Sensing Image Classification Based on Different Convolutional Neural Network Models

Remote sensing image classification is an important part of remote sensing data processing, and it plays an important role in both military strategy and civilian use. With the development of convolutional neural networks, there are more and more different remote sensing image classification algorithms. In order to improve the accuracy of remote sensing image classification, this paper uses GoogLeNet, ResNet and MobileNet three algorithms to perform remote sensing image classification experiments and optimizes parameters based on the UCM data set. At the same time, the data set is expanded by means of rotation and mirroring, and The pre-training model is loaded through the idea of transfer learning. The experimental results show that GoogleNet, ResNet50 and ResNet101 have the highest accuracy rates of 99.5%, while ResNet34, MobileNetV2 and MobileNetV3 algorithms have relatively low accuracy rates of 97.1%,93.8% and 96.7%, respectively. The accuracy of the expanded data set has increased by about 1.67% on average, which proves the effectiveness of data expansion in improving the algorithm's performance.