Online Target Recognition for Time-sensitive Space Information Networks
Huo CL(霍春雷); Huo CL(霍春雷)
2017-07
发表期刊IEEE Transactions on Computational Imaging
卷号1期号:1页码:1
摘要The key difficulties of online target recognition task for space information networks lie in the contradiction between  time-sensitive response requirement and resource constraints(e.g., computation resource, communication resource and training samples). To deal with the above problems, an effective online target recognizing approach is proposed, which seamlessly integrates fast online information processing task and efficient target-specific high-rate compression task. The proposed approach begins with enhancing the target-background separability by introducing intraclass and interclass couples, the new model adapted for  the hot spot image is then obtained  by capturing the relation between the online target data and the massive historical data.
The light-weight target-specific information is  efficiently transmitted to  the ground system, and the whole scene is capable of being recovered while the details of targets are being preserved. Compared with  traditional target recognition methods, the proposed approach is more promising for time-sensitive space information networks. The experiments demonstrate the effectiveness of the proposed approach.
关键词Target Recognition Space Information Networks Information Processing Information Compression Feature Adaption..
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12322
专题模式识别国家重点实验室_先进数据分析与学习
通讯作者Huo CL(霍春雷)
作者单位National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Huo CL,Huo CL. Online Target Recognition for Time-sensitive Space Information Networks[J]. IEEE Transactions on Computational Imaging,2017,1(1):1.
APA Huo CL,&霍春雷.(2017).Online Target Recognition for Time-sensitive Space Information Networks.IEEE Transactions on Computational Imaging,1(1),1.
MLA Huo CL,et al."Online Target Recognition for Time-sensitive Space Information Networks".IEEE Transactions on Computational Imaging 1.1(2017):1.
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