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Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks

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DOI: 10.23977/acsat.2017.1022


Iftene Meziane, Liu Qingjie, Wang Yunhong

Corresponding Author

Meziane Iftene


Recently, deep convolutional neural networks (CNNs) have made great achievements, whether taken as features extractor or classifier, in particular for very high resolution (VHR) images classification task which is a key point in the remote sensing field. This work aims to improve the VHR image classification accuracy by exploiting the fusion of two pre-trained deep convolutional neural network models. In this paper, we propose to concatenate the features extracted from the last convolutional layer of each pre-trained deep convolutional neural network to get a long features vector which is fed into a fully-connected layer and then perform a fine-tuning for a VHR image dataset classification. The experimental results are promising since they show that the fusion of two deep CNNs achieves better accuracy for the classification compared to the individual CNN models on the same dataset.


CNN Fusion, convolutional neural networks, fine-tuning, VHR images, classification.

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