Deep learning techniques for remote sensing image scene classification: A comprehensive review, current challenges, and future directions

Summary Since last decade, deep learning has made exceptional progress in various fields of artificial intelligence including image and voice recognition, natural language processing. Inspired by these successes, researchers are now applying deep learning techniques to classification of scenes in remote sensing images. The purpose of remote sensing image scene classification is to classify remote sensing scenes according to their content. These images display a complex structure due to the variety of landforms as well as the distance between the image collection instrument and earth. In our review, we discussed 76 relevant papers published on this topic over the past 6 years. The review conducts a comparison analysis based on the overall accuracy parameter to provide insight into the effectiveness of different methods on different proportions of the dataset. The five classes of techniques we describe are convolutional neural networks, autoencoders, generative adversarial networks, vision transformers, and few‐shot learning. Future directions are discussed in this review in order to enhance the effectiveness of deep learning‐based scene classification approaches. This article concludes with an overview of the proposed method to enhance the accuracy in classifying remote sensing images.