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Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

文献类型：期刊论文

作者Li, Feimo1,2; Li, Shuxiao1,2; Zhu, Chengfei1,2; Lan, Xiaosong1,2; Chang, Hongxing1,2; 李非墨,李书晓
刊名REMOTE SENSING
出版日期2017-05-01
卷号9期号:5页码:494
关键词Vehicle Localization Vehicle Classification High Resolution Aerial Image Convolutional Neural Network (Cnn) Class Imbalance
DOI10.3390/rs9050494
文献子类Article
英文摘要Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.
WOS关键词SCENE CLASSIFICATION ; FEATURES
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000402573700097
资助机构National Science Foundation of China (NSFC)(61302154 ; 61573350)
源URL[http://ir.ia.ac.cn/handle/173211/14579]  
专题自动化研究所_综合信息系统研究中心
通讯作者李非墨,李书晓
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Li, Feimo,Li, Shuxiao,Zhu, Chengfei,et al. Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images[J]. REMOTE SENSING,2017,9(5):494.
APA Li, Feimo,Li, Shuxiao,Zhu, Chengfei,Lan, Xiaosong,Chang, Hongxing,&李非墨,李书晓.(2017).Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images.REMOTE SENSING,9(5),494.
MLA Li, Feimo,et al."Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images".REMOTE SENSING 9.5(2017):494.

入库方式： OAI收割

来源：自动化研究所

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