Abstract
In order to improve the recognition accuracy of ground-based cloud image, a new algorithm based on deep learning is proposed. In our approach, a large number of cloud images are generated by Generative Adversarial Networks first. Then, based on the original and these generate cloud images, the deep features of cloud images are extracted automatically by multi-layer automatic sensing feature network, which increase the features description ability effectively. Finally, the Support Vector Machine (SVM) classifier is trained and the cloud image recognition is completed. Comparing with the methods such as gray level co-occurrence matrix (GLCM) and PCA with original database only, our approach combines the advantages of both GAN and PCANet, and the experiment results shows that the accuracy of cloud image recognition is significantly improved.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61802199) and the Student Practice Innovation Training Program Fund of Nanjing University of Information Science and Technology (Grant No. 2017103000170).
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Zhao, L., Lin, Y., Zhang, Z., Wang, S. (2019). Ground-Based Cloud Images Recognition Based on GAN and PCANet. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_34
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