中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A Comparison: Different DCNN Models for Intelligent Object Detection in Remote Sensing Images

文献类型:期刊论文

作者Ding, Peng; Zhang, Ye; Jia, Ping; Chang, Xu-ling
刊名Neural Processing Letters
出版日期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
DOI10.1007/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
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.

入库方式: OAI收割

来源:长春光学精密机械与物理研究所

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