Review on Vision Transformer for Satellite Image Classification

Satellite image classification has been a topic of interest in the research community for the last two decades. Neural network researchers have given advanced models year by year for this problem. A lot of attention is given to the transformers from 2017 onwards and the vision transformer is a transformer-based model for computer vision problems. This paper reviews the application of vision transformers (and its variants) to the satellite image classification. The article provides a detailed working of the vision transformer and the history of its chronological development. The review prospects that the vision transformer is suitable for a sufficiently large dataset. For small datasets, convolutional network-based models perform well as compared to vision transformers, but pre-trained vision transformers beating convolutional models and transfer learning have produced better results. The review suggests, considering limited research in the field of vision transformer application for satellite data, due to the fairly new model, there are high possibilities for making this model producing good results. The article also explores the ongoing challenges and research opportunities in the vision transformer development for satellite image classification.