The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval

Yun GE; Lin MA; Shunliang JIANG; Famao YE

doi:10.11999/JEIT190017

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2487-2494

Yun GE, Lin MA, Shunliang JIANG, Famao YE. The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017

Citation:

Yun GE, Lin MA, Shunliang JIANG, Famao YE. The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2487-2494. doi: 10.11999/JEIT190017

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The Combination and Pooling Based on High-level Feature Map for High-resolution Remote Sensing Image Retrieval

doi: 10.11999/JEIT190017

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

Funds: The National Natural Science Foundation of China (41801288, 41261091, 61662044, 61663031, 61762067)

Received Date: 2019-01-09
Rev Recd Date: 2019-06-18

Available Online: 2019-06-25

Publish Date: 2019-10-01

Abstract

Abstract

High-resolution remote sensing images have complex visual contents, and extracting feature to represent image content accurately is the key to improving image retrieval performance. Convolutional Neural Networks (CNN) have strong transfer learning ability, and the high-level features of CNN can be efficiently transferred to high-resolution remote sensing images. In order to make full use of the advantages of high-level features, a combination and pooling method based on high-level feature maps is proposed to fuse high-level features from different CNNs. Firstly, the high-level features are adopted as special convolutional features to preserve the feature maps of the high-level outputs under different input sizes, and then the feature maps are combined into a larger feature map to integrate the features learned by different CNNs. The combined feature map is compressed by max-pooling method to extract salient features. Finally, the Principal Component Analysis (PCA) is utilized to reduce the redundancy of the salient features. The experimental results show that compared with the existing retrieval methods, the features extracted by this method have advantages in retrieval efficiency and precision.
- Remote sensing image retrieval,
- Transfer learning,
- High-level feature map,
- Combination,
- Pooling

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References(20)

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