用文字描述代替高清图像传输，节省98%带宽。
Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction
- 用低分辨率图+文本摘要替代全尺寸图像传输
- 重建峰值信噪比达16.36~27.41 dB，保留关键信息
- 适合卫星通信、遥感数据高效传输场景
高分辨率遥感图像对环境监测、城市测绘和土地利用分析至关重要，但其传输常受带宽限制和通信成本制约。传统方法传输完整像素数据，造成冗余与低效。本文提出一种文本引导的遥感图像传输框架，将完整高分辨率数据替换为低分辨率图像与紧凑文本描述。机载文本生成器生成空间与语义摘要，使传输数据量降至原始大小的约2%。地面端采用文本条件图像恢复模型，通过跨模态学习恢复精细空间细节并保持语义一致性。在Alsat-2B、UC Merced Land Use和Aerial Image数据集上的实验表明，该框架重建的PSNR分别为16.36 dB、26.87 dB和27.41 dB，实现了遥感应用中高效且信息保真的图像传输。代码将公开于GitHub。
原文摘要 · Abstract (English)
High-resolution remote sensing imagery is critical for environmental monitoring, urban mapping, and land cover analysis, but its transmission is often hindered by limited bandwidth and high communication costs. Conventional pipelines transmit full-resolution pixel data, resulting in redundant and inefficient delivery. This paper proposes a text-guided remote sensing image transmission system that replaces complete high-resolution data with low-resolution images accompanied by compact textual descriptions. An onboard text generator produces spatial and semantic summaries, reducing the transmitted data volume to approximately 2\% of the original size. For ground-based reconstruction, a text-conditioned image restoration model is introduced, which leverages cross-modal learning to recover fine spatial details and maintain semantic coherence. Experimental results on the Alsat-2B, UC Merced Land Use, and Aerial Image datasets demonstrate that the proposed framework achieves reconstruction PSNRs of 16.36 dB, 26.87 dB, and 27.41 dB, respectively, enabling efficient and information-preserving image transfer for remote sensing applications. The implementation will be made publicly available at \href{https://github.com/haoyangofficial/textrssr}{GitHub}.
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