Journal Article

Transformers for Remote Sensing: A Systematic Review and Analysis

Chang, Ming, Yan, Ziyun, He, Guangjun, Johnson, Brian, Ma, Lei, Wang, Ruikun, Liang, Ying

2024

Overview

Research on transformers in remote sensing (RS), which started to increase after 2021, is facing the problem of a relative lack of review. To understand the trends of transformers in RS, we undertook a quantitative analysis of the major research on transformers over the past two years by dividing the application of transformers into eight domains: land use/land cover (LULC) classification, segmentation, fusion, change detection, object detection, object recognition, registration, and others. Quantitative results show that transformers achieve a higher accuracy in LULC classification and fusion, with more stable performance in segmentation and object detection. Combining the analysis results on LULC classification and segmentation, we have found that transformers need more parameters than convolutional neural networks (CNNs). Additionally, further research is also needed regarding inference speed to improve transformers’ performance. It was determined that the most common application scenes for transformers in our database are urban, farmland, and water bodies. We also found that transformers are employed in the natural sciences such as agriculture and environmental protection rather than the humanities or economics. Finally, this work summarizes the analysis results of transformers in remote sensing obtained during the research process and provides a perspective on future directions of development.

• Publisher: MDPI AG,MDPI
• Subject: Agricultural land / change detection / Classification / Computational linguistics / convolutional neural network / Deep learning / Electric transformers / Land use / Language processing / Natural language interfaces / Neural networks / Quantitative analysis / recurrent neural networks (RNNs) / Registration / Remote sensing / segmentation / Semantics / Systematic Review