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

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.