Scene classification of remote sensing image based on deep convolutional neural network

Zhou Yang; Xiao-dong Mu; Feng-an Zhao

doi:10.1117/12.2502942

9 August 2018 Scene classification of remote sensing image based on deep convolutional neural network

Zhou Yang, Xiao-dong Mu, Feng-an Zhao

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108064V (2018) https://doi.org/10.1117/12.2502942
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Zhou Yang, Xiao-dong Mu, and Feng-an Zhao "Scene classification of remote sensing image based on deep convolutional neural network", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108064V (9 August 2018); https://doi.org/10.1117/12.2502942

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KEYWORDS

Remote sensing

Scene classification

Data modeling

Image classification

Convolutional neural networks

Image fusion

Feature extraction

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