Remote Sensing Image Scene Classification Based on Global–Local Dual-Branch Structure Model

Abstract

Scene classification of high-resolution images is an active research topic in the remote sensing community. Although convolutional neural network (CNN)-based methods have obtained good performance, large-scale changes of ground objects in complex scenes restrict the further improvement of classification accuracy. In this letter, a global–local dual-branch structure (GLDBS) is designed to explore discriminative features of the original images and the crucial areas, and the strategy of decision-level fusion is applied for performance improvement. To discover the crucial area of the original image, the energy map generated by CNNs is transformed to the binary image, and the coordinates of the maximally connected region can be obtained. Among them, two shallow CNNs, ResNet18 and ResNet34, are selected as the backbone to construct a dual-branch network, and a joint loss is designed to optimize the whole model. In the GLDBS, the two streams employ the same structure (ResNet18-ResNet34) as the backbone, while the parameters are not shared. Experimental results on the aerial image data set (AID) and NWPU-RESISC45 datasets prove that the proposed GLDBS method achieves remarkable classification performance compared with some state-of-the-art (SOTA) methods. The highest overall accuracies (OAs) on the AID and NWPU-RESISC45 datasets are 97.01% and 94.46%, respectively.