MATEC Web Conf.
Volume 336, 20212020 2nd International Conference on Computer Science Communication and Network Security (CSCNS2020)
|Number of page(s)||6|
|Section||Artificial Recognition and Application|
|Published online||15 February 2021|
Scene classification of remote sensing image based on compound pruning
1 Science and Technology College of Gannan Normal University, GanZhou, JiangXi, China 341000
2 School of Electrical Engineering and Automation, JiangXi University of Science and Technology, GanZhou, JiangXi, China 341000
* Corresponding author: firstname.lastname@example.org
Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.
© The Authors, published by EDP Sciences, 2021
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