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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Detection of Event of Interest for Satellite Video Understanding
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Detection of Event of Interest for Satellite Video Understanding

机译：检测卫星视频理解感兴趣的事件

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摘要

Satellite videos provide rich dynamic information of observed scenes at a large spatial and temporal scale and will play an important role in the future space information network. This work devotes to revealing events of interest (EOI) from satellite video scenes by using a two-stream method. In satellite videos, individual frames reflect the static information like the basic scenes where the event was happening, while a sequence of frames determines the motion information. Considering these facts, a novel two-stream EOI detection framework is proposed, where one stream extracts static spatial information of satellite videos by AlexNet, whereas the other stream extracts the motion information using a local trajectories analysis method. First, the whole video scene is segmented into small spatial-temporal patches, where labeling EOI and non-EOI is completed. Next, the trajectories are extracted from 3-D satellite video cubes that are generated from event scene patches. Finally, this trajectory classification process is treated as a weak supervision learning problem and solved by sparse dictionary learning. The experimental results demonstrate that the proposed two-stream method is effective for EOI detection and has a huge potential for satellite video scenes analysis and understanding. The proposed method also outperforms the existing competitive models for video analysis.
机译：卫星视频以大量空间和时间量表提供了观察到的场景的丰富动态信息，并将在未来的空间信息网络中发挥重要作用。这项工作旨在通过使用双流方法揭示来自卫星视频场景的感兴趣事件（EOI）。在卫星视频中，单个帧反映了像事件发生的基本场景等静态信息，而一系列帧确定运动信息。考虑到这些事实，提出了一种新的两流EOI检测框架，其中一个流通过AlexNet提取卫星视频的静态空间信息，而另一个流使用局部轨迹分析方法提取运动信息。首先，整个视频场景被分段为小空间贴片，其中标记EOI和非EOI完成。接下来，从事件场景补丁中生成的3-D卫星视频多维数据集中提取轨迹。最后，这个轨迹分类过程被视为弱监督学习问题，并通过稀疏的字典学习解决。实验结果表明，所提出的双流方法对于EOI检测有效，并且具有巨大的卫星视频场景的潜力分析和理解。该方法还优于现有的视频分析竞争模型。

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