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	A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images
	Ding, Peng; Zhang, Ye; Jia, Ping; Chang, Xu-ling
刊名	Neural Processing Letters
	2018
卷号	0页码:2019-01-11
关键词	Remote sensing Computer vision Convolution Deep learning Deep neural networks Neural networks Object detection Object recognition Space optics
ISSN号	13704621
DOI	10.1007/s11063-018-9878-5
英文摘要	In recent years, deep learning especially deep convolutional neural networks (DCNN) has made great progress. Many researchers take advantage of different DCNN models to do object detection in remote sensing. Different DCNN models have different advantages and disadvantages. But in the field of remote sensing, many scholars usually do comparison between DCNN models and traditional machine learning. In this paper, we compare different state-of-the-art DCNN models mainly over two publicly available remote sensing datasetsairplane dataset and car dataset. Such comparison can provide guidance for related researchers. Besides,we provide suggestions for fine-tuning different DCNN models. Moreover, for DCNN models including fully connected layers, we provide a method to save storage space. 2018 Springer Science+Business Media, LLC, part of Springer Nature
内容类型	期刊论文
源URL	[http://ir.ciomp.ac.cn/handle/181722/60557]
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Ding, Peng,Zhang, Ye,Jia, Ping,et al. A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images[J]. Neural Processing Letters,2018,0:2019-01-11.
APA	Ding, Peng,Zhang, Ye,Jia, Ping,&Chang, Xu-ling.(2018).A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images.Neural Processing Letters,0,2019-01-11.
MLA	Ding, Peng,et al."A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images".Neural Processing Letters 0(2018):2019-01-11.

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