A Double Knowledge Distillation Model for Remote Sensing Image Scene Classification

LI Daxiang; NAN Yixuan; LIU Ying

doi:10.11999/JEIT221017

Volume 45 Issue 10

Oct. 2023

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LI Daxiang, NAN Yixuan, LIU Ying. A Double Knowledge Distillation Model for Remote Sensing Image Scene Classification[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3558-3567. doi: 10.11999/JEIT221017

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LI Daxiang, NAN Yixuan, LIU Ying. A Double Knowledge Distillation Model for Remote Sensing Image Scene Classification[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3558-3567. doi: 10.11999/JEIT221017

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A Double Knowledge Distillation Model for Remote Sensing Image Scene Classification

doi: 10.11999/JEIT221017

School of Telecommunication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China

Funds: The National Natural Science Foundation of China (62071379), The Natural Science Foundation of Shaanxi Province (2017KW-013), The Innovation Foundation of Xi’an University of Posts and Telecommunications (CXJJYL2021055, YJGJ201902)

Received Date: 2022-08-03
Rev Recd Date: 2023-01-15

Available Online: 2023-02-22

Publish Date: 2023-10-31

Abstract

Abstract

In order to improve the accuracy of light-weight Convolutional Neural Networks (CNN) in the classification task of Remote Sensing Images (RSI) scene, a Double Knowledge Distillation (DKD) model combined with Dual-Attention (DA) and Spatial Structure (SS) is designed in this paper. First, new DA and SS modules are constructed and introduced into ResNet101 and lightweight CNN designed as teacher and student networks respectively. Then, a DA distillation loss function is constructed to transfer DA knowledge from teacher network to student network, so as to enhance their ability to extract local features from RSI. Finally, constructing a SS distillation loss function, migrating the semantic extraction ability in the teacher network to the student network in the form of a spatial structure to enhance its ability to express the high -level semantics of the RSI. The experimental results based on two standard data sets AID and NWPU-45 show that the performance of the student network after knowledge distillation is improved by 7.57% and 7.28% respectively under the condition of 20% training proportion, and the performance is still better than other methods under the condition of fewer parameters.
- Remote sensing image classification,
- Knowledge distillation,
- Dual Attention (DA),
- Spatial Structure (SS)

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References(28)

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