A Review of Data Augmentation Methods of Remote Sensing Image Target Recognition

Abstract

In recent years, remote sensing target recognition algorithms based on deep learning technology have gradually become mainstream in the field of remote sensing because of the great improvements that have been made in the accuracy of image target recognition through the use of deep learning. In the research of remote sensing image target recognition based on deep learning, an insufficient number of research samples is often an encountered issue; too small a number of research samples will cause the phenomenon of an overfitting of the model. To solve this problem, data augmentation techniques have also been developed along with the popularity of deep learning, and many methods have been proposed. However, to date, there is no literature aimed at expounding and summarizing the current state of the research applied to data augmentation for remote sensing object recognition, which is the purpose of this article. First, based on the essential principles of data augmentation methods, the existing methods are divided into two categories: data-based data augmentation methods and network-based data augmentation methods. Second, this paper subdivides and compares each method category to show the advantages, disadvantages, and characteristics of each method. Finally, this paper discusses the limitations of the existing methods and points out future research directions for data augmentation methods.