Recognition and Classification of High Resolution Remote Sensing Image based on Convolutional Neural Network

doi:10.23940/ijpe.18.11.p31.28522863

Int J Performability Eng ›› 2018, Vol. 14 ›› Issue (11): 2852-2863.doi: 10.23940/ijpe.18.11.p31.28522863

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Recognition and Classification of High Resolution Remote Sensing Image based on Convolutional Neural Network

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 interests include machine learning algorithms, mainly for the application of machine learning in remote sensing image classication 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.

Abstract

Abstract: High resolution remote sensing image data is veritable big data. It is not only massive, multi-source, and heterogeneous, but also high-dimensional, multi-scale, and non-stationary. In order to overcome the reduction of classification accuracy and redundancy of spatial data when dealing with high resolution remote sensing images using traditional classification methods, this paper improves the traditional Convolution Neural Network (CNN) from the aspects of both the network structure and the training method, and the improved CNN is used in the classification and recognition of high resolution remote sensing images. The experiments show that the classification accuracy of the improved CNN is better than that of the traditional CNN. Furthermore, the classification accuracy of the improved CNN is better than the Deep Belief Network (DBN), Support Vector Machine (SVM), and traditional BP.

Key words: high resolution remote sensing image, convolution neural network, identification and classification

Guanyu Chen, Zhihua Cai, and Xiang Li. Recognition and Classification of High Resolution Remote Sensing Image based on Convolutional Neural Network [J]. Int J Performability Eng, 2018, 14(11): 2852-2863.

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Recognition and Classification of High Resolution Remote Sensing Image based on Convolutional Neural Network

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