Airplanes are the most popular investigation objects as a dynamic and critical component in remote sensing images. Accurately identifying and monitoring airplane behaviors is crucial for effective air traffic management. However, existing methods for interpreting fine-grained airplanes in remote sensing data depend heavily on large annotated datasets, which are both time-consuming and prone to errors due to the detailed nature of labeling individual points. In this paper, we introduce Text2AIRS, a novel method that generates fine-grained and realistic Airplane Images in Remote Sensing from textual descriptions. Text2AIRS significantly simplifies the process of generating diverse aircraft types, requiring limited texts and allowing for extensive variability in the generated images. Specifically, Text2AIRS is the first to incorporate ground sample distance into the text-to-image stable diffusion model, both at the data and feature levels. Extensive experiments demonstrate our Text2AIRS surpasses the state-of-the-art by a large margin on the Fair1M benchmark dataset. Furthermore, utilizing the fine-grained airplane images generated by Text2AIRS, the existing SOTA object detector achieves 6.12% performance improvement, showing the practical impact of our approach.
Yang et al. (Thu,) studied this question.