A Domain Adaptation Method for Land Use Classification Based on Improved HR-Net

Abstract

In recent years, the recognition accuracy of a semantic segmentation model on natural images can yield a very high level. Thus, it is of great significance to utilize semantic segmentation algorithm to obtain land use classification with remote sensing images. However, due to the large differences between natural images and remote sensing images, the standard semantic segmentation algorithm is not effective for land use classification of remote sensing images. In this article, the structure of high-resolution network (HR-Net) algorithm is improved according to the difference between the two kinds of images to make it more suitable for remote sensing images. Furthermore, in order to overcome the dependence of the semantic segmentation algorithm on a large number of high-quality prior data sets, some research experiments are conducted with the improved HR-Net domain adaptation model, and both of the adversarial domain adaptation model and the fusion domain adaptation model based on improved HR-Net and CycleGAN are designed to reduce the workload of manually labeling data. The extensive experimental results show that the classification of our improved HR-Net algorithm and the two domain adaptation models outperform other algorithms that demonstrates the effectiveness and superiority of our algorithms.