Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
	Chen, Xueyun; Xiang, Shiming; Liu, Cheng-Lin; Pan, Chun-Hong
发表期刊	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2014-10-01
卷号	11 期号:10 页码:1797-1801
文章类型	Article
摘要	Detecting small objects such as vehicles in satellite images is a difficult problem. Many features (such as histogram of oriented gradient, local binary pattern, scale-invariant feature transform, etc.) have been used to improve the performance of object detection, but mostly in simple environments such as those on roads. Kembhavi et al. proposed that no satisfactory accuracy has been achieved in complex environments such as the City of San Francisco. Deep convolutional neural networks (DNNs) can learn rich features from the training data automatically and has achieved state-of-the-art performance in many image classification databases. Though the DNN has shown robustness to distortion, it only extracts features of the same scale, and hence is insufficient to tolerate large-scale variance of object. In this letter, we present a hybrid DNN (HDNN), by dividing the maps of the last convolutional layer and the maxpooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes, to enable the HDNN to extract variable-scale features. Comparative experimental results indicate that our proposed HDNN significantly outperforms the traditional DNN on vehicle detection.
关键词	Deep Convolutional Neural Networks (Dnns) Hybrid Dnns (hDnns) Remote Sensing Vehicle Detection
WOS标题词	Science & Technology ; Physical Sciences ; Technology
关键词[WOS]	CLASSIFICATION ; RECOGNITION
收录类别	SCI
语种	英语
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:000337174100027
引用统计	被引频次：439[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/173211/3068
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	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,Xiang, Shiming,Liu, Cheng-Lin,et al. Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2014,11(10):1797-1801.
APA	Chen, Xueyun,Xiang, Shiming,Liu, Cheng-Lin,&Pan, Chun-Hong.(2014).Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,11(10),1797-1801.
MLA	Chen, Xueyun,et al."Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 11.10(2014):1797-1801.