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Building Regional Covariance Descriptors for Vehicle Detection
Chen, Xueyun1,2; Gong, Ren-Xi1,2; Xie, Ling-Ling1,2; Xiang, Shiming1,2; Liu, Cheng-Lin1,2; Pan, Chun-Hong1,2
Source PublicationIEEE GEOSCIENCE AND REMOTE SENSING LETTERS
2017-04-01
Volume14Issue:4Pages:524-528
SubtypeArticle
AbstractWe study the question of building regional covariance descriptors (RCDs) for vehicle detection from highresolution satellite images. A unified way is proposed to build RCD features by constant convolutional kernels in the forms of 2-D masks. Two novel formulas are designed to construct different RCD types based upon one or two convolutional masks, obtaining ten novel RCD features by four simple constant convolutional masks. Experiments show that such convolutional-mask- based RCDs outperform the previous image-derivative-based RCDs, the popular local binary patterns (LBPs), the histogram of oriented gradients (HOGs), and LBP+HOG. Furthermore, feeding to nonlinear support vector machines (SVMs) of two kernel types [L-1 kernel and radial basis function (RBF)], these RCDs outperform four known deep convolutional neural networks: AlexNet, GoogLeNet, CaffeNet, and LeNet, as well as their fine-tuned models by their well-trained weights of imageNet classification. Among three popular classic classifiers we have tested in the experiments, nonlinear SVMs outperform BP and Adaboost obviously, and L-1 kernel exceeds RBF slightly.
KeywordDeep Convolutional Neural Networks (Dcnns) Regional Covariance Descriptor (Rcd) Vehicle Detection
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2653772
WOS KeywordCLASSIFICATION ; IMAGES
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61661006 ; 61561007 ; 91646207)
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000399952000012
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15271
Collection空天信息研究中心
Affiliation1.Guangxi Univ, Nanning 530004, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Chen, Xueyun,Gong, Ren-Xi,Xie, Ling-Ling,et al. Building Regional Covariance Descriptors for Vehicle Detection[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2017,14(4):524-528.
APA Chen, Xueyun,Gong, Ren-Xi,Xie, Ling-Ling,Xiang, Shiming,Liu, Cheng-Lin,&Pan, Chun-Hong.(2017).Building Regional Covariance Descriptors for Vehicle Detection.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,14(4),524-528.
MLA Chen, Xueyun,et al."Building Regional Covariance Descriptors for Vehicle Detection".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 14.4(2017):524-528.
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