Remote Sensing Image Retrieval Using Pre-trained Convolutional Neural Networks Based on ImageNet

GE Yun; JIANG Shunliang; YE Famao; XU Qingyong; TANG Yiling

doi:10.13203/j.whugis20150498

Volume 43 Issue 1

Jan. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(1): 67-73. > DOI: 10.13203/j.whugis20150498

GE Yun, JIANG Shunliang, YE Famao, XU Qingyong, TANG Yiling. Remote Sensing Image Retrieval Using Pre-trained Convolutional Neural Networks Based on ImageNet[J]. Geomatics and Information Science of Wuhan University, 2018, 43(1): 67-73. DOI: 10.13203/j.whugis20150498

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Remote Sensing Image Retrieval Using Pre-trained Convolutional Neural Networks Based on ImageNet

1.
Information Engineering School, Nanchang University, Nanchang 330031, China
2.
Software School, Nanchang Hangkong University, Nanchang 330063, China

Funds:

The National Natural Science Foundation of China 41261091

the Youth Fund Project of Education Department of Jiangxi GJJ13482

the National Natural Science Foundation of Jiangxi 20151BAB207062

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Author Bio:
GE Yun, PhD candidate, lecturer, specializes in remote sensing image retrieval. E-mail: geyun@nchu.edu.cn
Corresponding author:
JIANG Shunliang, PhD, professor. E-mail: jiangshunliang@ncu.edu.cn
Received Date: May 08, 2016
Published Date: January 04, 2018

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Abstract

Abstract

High resolution remote sensing images have complicated content and abundant detail information. Large semantic gaps will occur as such images are difficult to describe using traditional shallow features. This paper proposes a method using four different CNNs pre-trained on ImageNet to in remote sensing image retrieval. High-level features are extracted from different layers of four CNNs. A Gaussian normalization method is adopted to normalize high-level features, and Euclidean distance is used as the similarity measurement. A serial of experiments carried on the UC-Merced and WHU-RS datasets show that CNN-M feature achieves the best retrieval performance with CNN features. Compared with the visual bag of words and global morphological texture descriptors, the mean average precision of CNN features was 15.7%-25.6% higher than that of shallow features. The average normalizedmodified retrieval rank of CNN features was 17%-22.1% lower than that of shallow features. Therefore the pre-trained convolutional neural network is effective for high-resolution remote sensing image retrieval.
- remote sensing image,
- retrieval,
- convolutional neural networks,
- pre-trained

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References

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Remote Sensing Image Retrieval Using Pre-trained Convolutional Neural Networks Based on ImageNet

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