Knowledge Commons of Institute of Automation,CAS
Building Regional Covariance Descriptors for Vehicle Detection | |
Chen, Xueyun1,2; Gong, Ren-Xi1,2; Xie, Ling-Ling1,2; Xiang, Shiming1,2![]() ![]() ![]() | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
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2017-04-01 | |
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 |
DOI | 10.1109/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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15271 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.Guangxi Univ, Nanning 530004, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 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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