基于ResNet18和随机森林的遥感图像复杂场景分类方法OA北大核心CSTPCD

Complex scene classification is a crucial aspect of remote sensing image interpretation.This paper achieves high-precision classification of complex scenes in remote sensing images by optimizing the ResNet18 deep residual network and Random Forest.First,data augmentation is used to expand the database,alleviating the overfitting problem caused by the limited number of training samples.Then,the ResNet18 deep residual network is employed to automatically extract scene features from the remote sensing images.Finally,a Random Forest classifier is used to accomplish the complex scene classification task.Experiments were conducted on the NWPU-RESISC45 and UC Merced Land Use databases.The results show that the scene classification accuracies of the proposed model are 98.86%and 99.17%,respectively.Compared to using the ResNet18 deep residual network alone,the proposed model improves classification accuracy by 3.36%and 1.71%,respectively.Moreover,in comparison with other scene classification methods,the proposed model improves classification accuracy by 5.23%and 1.55%,respectively.