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.
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