SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioning
This addresses the problem of interpreting complex satellite imagery for applications like environmental monitoring, though it appears incremental as it builds on existing transformer methods.
The paper tackles remote sensing image captioning by proposing a transformer-based architecture that integrates Static Expansion, Memory-Augmented Self-Attention, and Mesh Transformer techniques, achieving state-of-the-art performance on UCM-Caption and NWPU-Caption datasets.
Image captioning has emerged as a crucial task in the intersection of computer vision and natural language processing, enabling automated generation of descriptive text from visual content. In the context of remote sensing, image captioning plays a significant role in interpreting vast and complex satellite imagery, aiding applications such as environmental monitoring, disaster assessment, and urban planning. This motivates us, in this paper, to present a transformer based network architecture for remote sensing image captioning (RSIC) in which multiple techniques of Static Expansion, Memory-Augmented Self-Attention, Mesh Transformer are evaluated and integrated. We evaluate our proposed models using two benchmark remote sensing image datasets of UCM-Caption and NWPU-Caption. Our best model outperforms the state-of-the-art systems on most of evaluation metrics, which demonstrates potential to apply for real-life remote sensing image systems.