| 面向任务的高分辨率光学卫星遥感影像智能压缩方法 |
| |
| 引用本文： | 王密,项韶,肖晶.面向任务的高分辨率光学卫星遥感影像智能压缩方法[J].武汉大学学报(信息科学版),2022,47(8):1213-1219. |
| |
| 作者姓名： | 王密 项韶 肖晶 |
| |
| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室，湖北 武汉，430079 |
| |
| 基金项目： | 国家杰出青年科学基金61825103湖北省自然科学基金2020CFA001湖北省重点研发计划2020BIB006 |
| |
| 摘 要： | 随着对地观测系统以及空间信息网络的快速发展，中国已经建成星地一体化的对地观测系统。高分辨率遥感影像数据从GB级转向TB级，轻小型智能遥感卫星有限的带宽容量和存储空间都严重限制了遥感信息的智能实时服务，由此提出了一种面向任务的智能压缩方法。首先，基于遥感影像的数据特点以及轻小型智能遥感卫星星地数传的瓶颈，分析了传统在轨压缩算法的局限性，论述了面向任务的高分辨率光学卫星遥感影像智能压缩处理的重要性；其次，提出了基于珞珈三号01星平台面向任务的智能压缩方法，通过星上高质量成像和高精度几何定位获取观测区域；然后，根据不同的任务需求，利用信息提取模型获取感兴趣目标/区域；最后，利用压缩模型对该区域进行自适应码率分配来实现高倍率压缩任务，并生成码流文件回传到地面。针对不同的任务需求，合理分配码率，可通过该方法有效实现遥感影像的高倍率智能压缩。
|
| 关 键 词： | 对地观测系统 高分辨率光学卫星 智能实时服务 珞珈三号01星 面向任务的智能压缩 |
| 收稿时间： | 2022-03-23 |
Task-Oriented Intelligent Compression Method for High Resolution Optical Satellite Remote Sensing Image |
| |
| Affiliation: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.School of Computer Science, Wuhan University, Wuhan 430072, China |
| |
| Abstract: | Objectives With the rapid development of earth observation system and space information network, the integration system of space and earth observation has been built in China. High-resolution remote sensing data have changed from GB-level to TB-level. The limited bandwidth capacity and storage space on the satellite seriously limit the development of the intelligent and real-time service based on remote sensing information. Methods Firstly, we introduce the characteristics of remote sensing data and the bottleneck of satellite-ground data transmission. Secondly, the limitations of traditional on-orbit compression algorithm are presented, we further discuss the importance of using high-ratio intelligent compression processing to realize low latency data transmission. Then, we introduce task-oriented intelligent compression method and procedure on Luojia-3(01) satellite. The compression framework obtains the observation region through high-quality imaging and high-precision geometric positioning, and captures the region of interest (ROI) using information extraction model. Finally, the mask of ROI is used to guide the compression model to achieve adaptive bit-rate allocation, and generate bits-stream file for transmission to the ground. Results According to the requirements of different tasks, using adaptive bits allocation can realize the intelligent compression of remote sensing image with high compression ratio, so as to realize the fast data transmission between satellite and ground. Conclusions Luojia-3(01) satellite has an extensible application software module that provides a common data interface, and provides an on-orbit verification environment for high-ratio compression algorithm, which is of great significance to the industrialization and commercialization of remote sensing technology. |
| |
| Keywords: | |
|
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
|
