Scene classification of remote sensing image based on compound pruning

Fengbing Jiang,Fang Li,Guoliang Yang,I Barukčić

doi:10.1051/matecconf/202133606030

Abstract

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.

Highlights

The task of remote sensing image scene classification is related to many applications
The accuracy of remote sensing image scene classification based on convolution neural network has been greatly improved
In order to verify the effectiveness of the compound pruning method proposed in this paper, the algorithm is tested on the remote sensing data set NWPU-RESISC45, and compared with other pruning methods

Summary

Introduction

The task of remote sensing image scene classification is related to many applications. The channel pruning method makes the scaling factor of the corresponding batch specification layer of the secondary channel close to zero through sparse training strategy, and reduces the model parameters by pruning, and its performance is verified by experiments. The deficiency of this method is that the algorithm is sensitive to hyperparameters, and because of the introduction of the channel selection layer, the network must be transformed into ONNX format in order to deploy effectively. By defining the elastic variable for the convolution layer, a new compound pruning method is proposed by using the filter elastic variable and the batch normalized layer scaling factor. No new network layer will be added after pruning

Convolution neural network compound pruning

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Experimental results and analysis

Experimental results

Conclusion