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Scene classification of remote sensing image based on deep convolutional neural network
Author(s): Zhou Yang; Xiao-dong Mu; Feng-an Zhao
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Paper Abstract

Aiming at low precision of remote sensing image scene classification, a classification method DCNN_FF based on deep convolutional neural network (DCNN) feature fusion (FF) is proposed. This method utilizes the existing pre-trained network models CaffeNet and GoogLeNet, and extracts the features of the classified remote sensing images by fine tuning on the target dataset. After dimension reduction by principle component analysis (PCA), the features extracted from the two network models are combined. Finally, the support vector machine (SVM) is used for classification of the combined features. The experimental results on the commonly used and latest datasets show that, this method can utilize the existing network models and combine with the structural advantages of different models, and its average classification accuracy is higher than that of single network model by more than 1.68%. Thus it improves the accuracy of remote sensing image scene classification.

Paper Details

Date Published: 9 August 2018
PDF: 9 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064V (9 August 2018); doi: 10.1117/12.2502942
Show Author Affiliations
Zhou Yang, Xi’an High Technology Research Institute (China)
Xiao-dong Mu, Xi’an High Technology Research Institute (China)
Feng-an Zhao, Xi’an High Technology Research Institute (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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