SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioning

• URL: http://arxiv.org/abs/2507.12845v1
• Date: Thu, 17 Jul 2025 07:11:01 GMT
• Title: SEMT: Static-Expansion-Mesh Transformer Network Architecture for Remote Sensing Image Captioning
• Authors: Khang Truong, Lam Pham, Hieu Tang, Jasmin Lampert, Martin Boyer, Son Phan, Truong Nguyen,
• Abstract summary: We present a transformer based network architecture for remote sensing image captioning (RSIC)<n>We evaluate our proposed models using two benchmark remote sensing image datasets of UCM-Caption and NWPU-Caption.
• Score: 2.2184293265652895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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