Siamese Convolutional Neural Networks for Remote Sensing Scene Classification

The convolutional neural networks (CNNs) have shown powerful feature representation capability, which provides novel avenues to improve scene classification of remote sensing imagery. Although we can acquire large collections of satellite images, the lack of rich label information is still a major c...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2019-08, Vol.16 (8), p.1200-1204
Main Authors: Liu, Xuning, Zhou, Yong, Zhao, Jiaqi, Yao, Rui, Liu, Bing, Zheng, Yi
Format: Article
Language:English
Subjects:
Artificial neural networks
Classification
Collections
Computational modeling
Computer architecture
convolutional neural networks (CNNs)
Data collection
Data models
Datasets
Deep learning
Detection
Feature extraction
Image acquisition
Image classification
Imagery
Measurement
Neural networks
Performance evaluation
Regularization
Remote sensing
Satellite imagery
Satellites
Spaceborne remote sensing
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Summary:The convolutional neural networks (CNNs) have shown powerful feature representation capability, which provides novel avenues to improve scene classification of remote sensing imagery. Although we can acquire large collections of satellite images, the lack of rich label information is still a major concern in the remote sensing field. In addition, remote sensing data sets have their own limitations, such as the small scale of scene classes and lack of image diversity. To mitigate the impact of the existing problems, a Siamese CNN, which combines the identification and verification models of CNNs, is proposed in this letter. A metric learning regularization term is explicitly imposed on the features learned through CNNs, which enforce the Siamese networks to be more robust. We carried out experiments on three widely used remote sensing data sets for performance evaluation. Experimental results show that our proposed method outperforms the existing methods.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2019.2894399