Abstract
Satellite Clouds have a significant role in the weather system and climate change, and the distribution of clouds is always strongly tied to a particular meteorological phenomenon. In this paper, an automatic identification of cloud types is proposed using a hybrid approach of convolution neural network (CNN) and bidirectional character based long short-term memory (LSTM). The large-scale cloud image database for meteorological research (LSCIDMR) of the ground truth images related to weather types is used as the input for the proposed work. Three types of CNN models, such as inception v3 network, Vgg-16 and Alexnet, are used separately and subsequently, the results are compared, in terms of precision, recall, and F1 score, to obtain the best among them. The LSTM is trained with our self-trained dictionary having tokens. The image features and single character are merged into a single step. It produces the output as the next character to come and so on.
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References
Ye, L., Cao, Z., Xiao, Y.: DeepCloud: ground-based cloud image categorization using deep convolutional features. IEEE Trans. Geosci. Remote Sens. 55(10), 5729–5740 (2017)
Jin, W., Gong, F., Tang, B., Wang, S.: Cloud types identification for meteorological satellite image using multiple sparse representation classifiers via decision fusion. IEEE Access 7, 8675–8688 (2019)
Phung, V.H., Rhee, E.J.: A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl. Sci. 9 (2019)
Jeppesen, J.H., Jacobsen, R.H., Inceoglu, F., Toftegaard, T.S.: A cloud detection algorithm for satellite imagery based on deep learning. Remote Sens. Environ. 229, 247–259 (2019). ISSN 0034-4257
Segal-Rozenhaimer, M., Li, A., Das, K., Chirayath, V.: Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sens. Environ. 237 (2020)
Mahajan, S., Fataniya, B.: Cloud detection methodologies: variants and development—a review. Complex Intell. Syst. 6, 251–261 (2020)
Ahendyarti, C., Wiryadinata, R., Rohana, N., Muhammad, F.: Cloud classification from NOAA satellite image using learning vector quantization method. In: 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE), pp. 97–100 (2020)
Ahmed, T., Sabab, N.: Classification and understanding of cloud structures via satellite images with EfficientUNet (2020). https://doi.org/10.1002/essoar.10507423
Bai, C., Zhang, M., Zhang, J., Zheng, J., Chen, S.: LSCIDMR: large-scale satellite cloud image database for meteorological research. IEEE Trans. Cybern. 52(11), 12538–12550 (2022)
Jiao, W., Zhang, Y., Zhang, B., Wan, Y.: SCTrans: a transformer network based on the spatial and channel attention for cloud detection. In: IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, pp. 615–618 (2022). https://doi.org/10.1109/IGARSS46834.2022.9883360
Gupta, R., Nanda, S.J.: Cloud detection in satellite images with classical and deep neural network approach: a review. Multimed Tools Appl. 81, 31847–31880 (2022). https://doi.org/10.1007/s11042-022-12078-w
Lv, Q., Li, Q., Chen, K., Lu, Y., Wang, L.: Classification of ground-based cloud images by contrastive self-supervised learning. Remote Sens. 14, 5821 (2022). https://doi.org/10.3390/rs14225821
Romero Jure, P., Masuelli, S., Cabral, J.: A labeled dataset of cloud types using data from GOES-16 and CloudSat. In: 2022 IEEE Biennial Congress of Argentina (ARGENCON), San Juan, Argentina, pp. 1–6 (2022). https://doi.org/10.1109/ARGENCON55245.2022.9940053
Alzubaidi, L., Zhang, J., Humaidi, A.J., et al.: Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data 8, 53 (2021)
Alawneh, L., Mohsen, B., Al-Zinati, M., Shatnawi, A., Al-Ayyoub, M.: A comparison of unidirectional and bidirectional LSTM networks for human activity recognition. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 1–6 (2020)
Balasingam, B., Bar-Shalom, Y., Willett, P., Pattipati, K.: Maximum likelihood detection on images. In: 2017 20th International Conference on Information Fusion (Fusion) (2017)
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Mishra, S., Guhathakurta, P.K. (2024). Identification of Cloud Types for Meteorological Satellite Images: A Character-Based CNN-LSTM Hybrid Caption Model. In: Dasgupta, K., Mukhopadhyay, S., Mandal, J.K., Dutta, P. (eds) Computational Intelligence in Communications and Business Analytics. CICBA 2023. Communications in Computer and Information Science, vol 1955. Springer, Cham. https://doi.org/10.1007/978-3-031-48876-4_15
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DOI: https://doi.org/10.1007/978-3-031-48876-4_15
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