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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
2017-04-01
发表期刊IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷号14期号:4页码:524-528
文章类型Article
摘要We 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.
关键词Deep Convolutional Neural Networks (Dcnns) Regional Covariance Descriptor (Rcd) Vehicle Detection
WOS标题词Science & Technology ; Physical Sciences ; Technology
DOI10.1109/LGRS.2017.2653772
关键词[WOS]CLASSIFICATION ; IMAGES
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61661006 ; 61561007 ; 91646207)
WOS研究方向Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000399952000012
引用统计
被引频次:3[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15271
专题空天信息研究中心
作者单位1.Guangxi Univ, Nanning 530004, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
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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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