Abstract
Cloud classification is an important and challenging task in cloud observation technology. For better classification, we present a method based on multi-task learning using multi-modal information. We utilize different loss functions to conduct multi-task learning. We implement a series of experiments on multi-modal ground-based cloud datasets for different tasks. Experimental results show that multi-task learning is effective for cloud image classification using multi-modal information, and it can improve the results of cloud image classification.
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References
Singh, M., Glennen, M.: Automated ground-based cloud recognition. Patt. Anal. Appl. 8(3), 258–271 (2005)
Xiao, Y., Cao, Z., Zhuo, W., Ye, L., Zhu, L.: A multiview visual feature extraction mechanism for ground-based cloud image categorization. J. Atmos. Oceanic Technol. 33(4), 789–801 (2016)
Oikonomou, S., Kazantzidis, A., Economou, G., Fotopoulos, S.: A local binary pattern classification approach for cloud types derived from all-sky imagers. Int. J. Remote Sens. 49(7), 2667–2682 (2019)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 971–987 (2002)
Zhang, Z., Wang, C., Xiao, B., Zhou, W., Liu, S., Shi, C.: Cross-view action recognition via a continuous virtual path. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2690–2697 (2013)
Shuang, L., Mei, L., Zhong, Z., Baihua, X., Xiaozhong, C.: Multimodal ground-based cloud classification using joint fusion convolutional neural network. Remote Sens. 10(6), 822 (2018)
Shuang, L., Linbo, Z., Zhong, Z., Chunheng, W., Baihua, X.: Automatic cloud detection for all-sky images using superpixel segmentation. IEEE Geosci. Remote Sens. Lett. 12(2), 354–358 (2014)
Zhang, Z., Li, D., Liu, S.: Salient dual activations aggregation for ground-based cloud classification in weather station networks. IEEE Access 6, 59173–59181 (2018)
Theckedath, D., Sedamkar, R.R.: Detecting affect states using VGG16, ResNet50 and SE-ResNet50. SN Comput. Sci. 1(2), 1–7 (2020)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)
Da, K.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2015)
Acknowledgement
This work was supported by College Student Research and Career-creation Program of Tianjin City for Undergraduates under Grant No. 202010065088, Natural Science Foundation of Tianjin under Grant No. 20JCZDJC00180 and No. 19JCZDJC31500, the Open Projects Program of National Laboratory of Pattern Recognition under Grant No. 202000002, and the Tianjin Higher Education Creative Team Funds Program.
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Zhang, Y. et al. (2022). Cloud Type Classification Using Multi-modal Information Based on Multi-task Learning. In: Liang, Q., Wang, W., Liu, X., Na, Z., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2021. Lecture Notes in Electrical Engineering, vol 878. Springer, Singapore. https://doi.org/10.1007/978-981-19-0390-8_15
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DOI: https://doi.org/10.1007/978-981-19-0390-8_15
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