Classification of Remote Sensing Images based on Distributed Convolutional Neural Network Model

doi:10.23940/ijpe.19.06.p2.15081517

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (6): 1508-1517.doi: 10.23940/ijpe.19.06.p2.15081517

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Classification of Remote Sensing Images based on Distributed Convolutional Neural Network Model

Guanyu Chen^a,b, Zhihua Cai^a,b, and Xiang Li^a,b,*

a School of Computer Science, China University of Geosciences, Wuhan, 430074, China
b Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China

Submitted on ;
Contact: * E-mail address: lixiang@cug.edu.cn
About author:Guanyu Chen received his M.S. degree in computer science from China University of Geosciences. He is a Ph.D. candidate of geoscience information engineering at China University of Geosciences. His research interest is machine learning algorithms, mainly for the application of machine learning in remote sensing image classification and recognition;Zhihua Cai received his Ph.D. in geodetection and information technology from China University of Geosciences. He is a professor of geoscience information engineering at China University of Geosciences. His research interests include data mining and evolutionary computation;Xiang Li received his Ph.D. in computer science from China University of Geosciences. He is a professor of computer science at China University of Geosciences. His research interests include remote sensing image recognition and differential evolution.
Supported by:
This work was partly supported by the National Natural Science Foundation of China (No. 61573324) and the State Key Laboratory of Intelligent Control and Decision of Complex System. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Natural Science Foundation.

Abstract

Abstract: With the network model architecture of Google Inception, research is conducted on issues such as the structural design of the model, data preprocessing, tuning of training parameters, computing clusters in a distributed environment, and multi-machine parallel training. According to the performances of different deep neural network models on different data sets, the Google Inception V3 depth network model is used as the prototype to conduct the tuning of training parameters, and the classification of remote sensing images is then realized with this model in the single-machine environment. Furthermore, due to the effectiveness of distributed systems for very large data sets and compute-intensive applications, a data parallel training scheme based on the distributed platform is designed for the convolution neural network model with more complex data form, larger quantity of parameters, and more network levels, after studying the mainstream designs of the distributed machine learning and analyzing the training methods and steps of the convolutional neural network model in a multi-machine environment. It greatly improves the training time of the model, and then the classification of remote sensing images under distributed clusters is realized.

Key words: CNN, remote sensing image, distributed cluster, parallel training, TensorFlow

Guanyu Chen, Zhihua Cai, and Xiang Li. Classification of Remote Sensing Images based on Distributed Convolutional Neural Network Model [J]. Int J Performability Eng, 2019, 15(6): 1508-1517.

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Classification of Remote Sensing Images based on Distributed Convolutional Neural Network Model

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