基于胶囊网络及其权重剪枝的SAR图像变化检测方法

doi:10.11896/jsjkx.200800225

Computer Science ›› 2021, Vol. 48 ›› Issue (7): 190-198.doi: 10.11896/jsjkx.200800225

• Computer Graphics & Multimedia • Previous Articles Next Articles

SAR Image Change Detection Method Based on Capsule Network with Weight Pruning

CHEN Zhi-wen¹, WANG Kun¹, ZHOU Guang-yun², WANG Xu², ZHANG Xiao-dan², ZHU Hu-ming¹

1 Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education,Xidian University,Xi’an 710071,China
2 Beijing Electro-mechanical Engineering Institute,Beijing 100074,China

Received:2020-08-31 Revised:2021-01-24 Online:2021-07-15 Published:2021-07-02
About author:CHEN Zhi-wen,born in 1995,postgra-duate.His main research interests include artificial intelligence computing system and its performance optimization.(18829535531@163.com)
ZHU Hu-ming,born in 1978,Ph.D,associate professor.His main research interests include high performance computing and parallel machine learning algorithm.
Supported by:
Cooperative Education Project of the Ministry of Education(201901159006) and National Defense Basic Scientific Research Program Funding(JCKY2016204A102).

Abstract

Abstract: The SAR image change detection algorithm based on deep neural network has been widely used in many fields such as agricultural detection,urban planning and forest early warning due to its high accuracy.This paper designs a SAR image change detection algorithm based on capsule network.In view of its high model complexity and large number of parameters,a model compression method based on weight pruning is proposed.This method performs layer-by-layer analysis of its capsule network parameters,adopts different pruning strategies for different types of layers,prunes redundant parameters in the network,and then fine-tunes the pruned network to improve the detection performance of the model.Finally,by compressing and storing the para-meters retained in the model,the storage space occupied by the model is significantly reduced.Experiments on four datasets of real SAR images prove the effectiveness of the proposed model compression method.

Key words: Capsule network, Change detection, Model compression, SAR image, Weight pruning

CLC Number:

TP391

CHEN Zhi-wen, WANG Kun, ZHOU Guang-yun, WANG Xu, ZHANG Xiao-dan, ZHU Hu-ming. SAR Image Change Detection Method Based on Capsule Network with Weight Pruning[J].Computer Science, 2021, 48(7): 190-198.

References

[1]LIU L,JIA Z,YANG J,et al.SAR Image Change DetectionBased on Mathematical Morphology and the K-Means Clustering Algorithm[J].IEEE Access,2019,7:43970-43978.
[2]SABOUR S,FROSST N,HINTON G E.Dynamic routing between capsules[C]//Advances in Neural Information Processing Systems.2017:3856-3866.
[3]DHILLON A,VERMA G K.Convolutional neural network:areview of models,methodologies and applications to object detection[J].Progress in Artificial Intelligence,2020,9(2):85-112.
[4]PAOLETTI M E,HAUT J M,FERNANDEZ-BELTRAN R,et al.Capsule Networks for Hyperspectral Image Classification[J].IEEE Transactions on Geoscience & Remote Sensing,2019,57(4):2145-2160.
[5]CHOI Y,EL-KHAMY M,LEE J.Universal Deep Neural Network Compression[J].IEEE Journal on Selected Topics in Signal Processing,2020,14(4):715-726.
[6]HOWARD A G,ZHU M,CHEN B ,et al.Mobilenets:Efficient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017.
[7]ZHANG T Y,YE S K,ZHANG K Q,et al.A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers[J].arXiv:1804.03294,2018.
[8]GUPTA S,AGRAWAL A,GOPALAKRISHNAN K,et al.Deep learning with limited numerical precision[C]//Internatio-nal Conference on Machine Learning.2015:1737-1746.
[9]SUN S,CHENG Y,GAN Z,et al.Patient Knowledge Distillation for BERT Model Compression[J].arXiv:1908.09355,2019.
[10]HUANG G,LIU Z,WEINBERGER K Q.Densely Connected Convolutional Networks[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR).2017:2261-2269.
[11]HU J,SHEN L,ALBANIE S,et al.Squeeze-and-Excitation Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(8):2011-2023.
[12]XIONG Y,SU G,YE S,et al.Deeper Capsule Network forComplex Data[C]//2019 International Joint Conference on Neural Networks (IJCNN).Budapest,Hungary,2019:1-8.
[13]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[14]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014.
[15]KRINIDIS S,CHATZIS V.A robust fuzzy local informationC-means clustering algorithm[J].IEEE Transactions on Image Processing,2010,19(5):1328-1337.
[16]HE Y,KANG G,DONG X,et al.Soft Filter Pruning for Acce-lerating Deep Convolutional Neural Networks[J].arXiv:1808.06866.
[17]PASANDI M M,HAJABDOLLAHI M,KARIMI N,et al.Mode-ling of Pruning Techniques for Deep Neural Networks Simplification[J].arXiv:2001.04062.
[18]VU V T,GOMES N R,PETTERSSON M I,et al.Bivariate Gamma Distribution for Wavelength-Resolution SAR Change Detection[J].IEEE Transactions on Geoscience and Remote Sensing,2019,57(1):473-481.

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