CIOMP OpenIR
A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images
Ding, Peng; Zhang, Ye; Jia, Ping; Chang, Xu-ling
2018
发表期刊Neural Processing Letters
ISSN13704621
卷号0页码:2019-01-11
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
关键词Remote sensing Computer vision Convolution Deep learning Deep neural networks Neural networks Object detection Object recognition Space optics
DOI10.1007/s11063-018-9878-5
收录类别EI
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文献类型期刊论文
条目标识符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
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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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