Recent Advances in Intelligent Processing of Satellite Video: Challenges, Methods, and Applications

Intelligent processing of satellite video focuses on extracting specific information of ground objects and scenes from earth observation videos through intelligent image/video processing technology, which has important applications in fields such as traffic monitoring, resource monitoring, and environmental monitoring. The integration of deep learning technology in satellite video processing has led to significant advancements in tasks such as object detection and object tracking, expanding into emerging research areas such as satellite video scene classification and object segmentation. However, there is no comprehensive review and summary in the intelligent processing of satellite video. This article presents a systematic review and quantitative analysis of the results published over the last decade, intending to further promote the development of various intelligent processing tasks for satellite video. It analyzes the current difficulties, challenges, and the methodological system for each task. In addition, it provides an in-depth analysis and summary of publicly available datasets and evaluation benchmarks for each task, as well as classic algorithm performance and application scenarios. Finally, this article summarizes the current research status and looks forward to the future development trend, hoping to inspire researchers in related fields and jointly promote the development of intelligent processing of satellite video.

Automated summary

Intelligent processing of satellite video focuses on extracting specific information of ground objects and scenes from earth observation videos through intelligent image/video processing technology. This article presents a systematic review and quantitative analysis of the results published over the last decade, intending to further promote the development of various intelligent processing tasks for satellite video. It analyzes the current difficulties, challenges, and methodological systems for each task, and also provides in-depth analysis and summary of publicly available datasets, algorithm performance, and application scenarios.