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Cloud-WAVECAP: Ground-based cloud types detection with an efficient wavelet-capsule approach

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Abstract

Cloud detection plays a significant role in various practices, such as weather forecasting, climate studies, etc. This paper presents an integrated approach called cloud-WAVECAP for ground-based cloud detection using a combination of wavelet and capsule networks. The proposed approach utilizes the wavelet’s multiscale analysis capability to detect significant cloud features at different resolutions. Meanwhile, the inherent capability of dynamic routing by the capsule network increases the model’s ability to capture hierarchical structures and spatial relationships within the clouds. The cloud-WAVECAP model applies two-level wavelet decomposition, followed by convolutional layers and the capsule network. This architecture integrates preprocessing, wavelet layers, and capsule layers to capture both low- and high-level features for accurate cloud classification. It excels in identifying different cloud types, which is vital for meteorological analysis. Cloud-WAVECAP is assessed using several metrics and outperforms Inception V3, VGGNet, Resnet50, EfficientNet-B7, achieving 98.42% precision, 98.48% recall, and 99.12% accuracy. Additionally, the model’s efficiency, measured by Floating Point Operations (FLOPs), is competitive, resulting in 1.5426 GIGA FLOPS compared to other methods.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Author information

Author notes

  1. Samarjit Kar and Parag Kumar Guhathakurta contributed equally to this work.

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Durgapur, India

    Sanjukta Mishra & Parag Kumar Guhathakurta

  2. Department of CST & CSIT, Institute of Engineering and Management (IEM), University of Engineering and Management, Kolkata, India

    Sanjukta Mishra

  3. Department of Mathematics, National Institute of Technology, Durgapur, India

    Samarjit Kar

  4. Department of Graphical Systems, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Samarjit Kar

Authors
  1. Sanjukta Mishra
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  2. Samarjit Kar
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Contributions

Sanjukta Mishra helped in conceptualization, methodology, formal analysis, writing—original draft, investigation. Samarjit Kar was involved in resources, formal analysis, project administration, supervision, writing—review & editing. Parag Kumar Guhathakurta helped in software, validation, supervision, data curation, writing—review & editing

Corresponding author

Correspondence to Sanjukta Mishra.

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Appendix A

Appendix A

Algorithm 1
figure a

Wavelet Capsule Model Construction

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Mishra, S., Kar, S. & Guhathakurta, P.K. Cloud-WAVECAP: Ground-based cloud types detection with an efficient wavelet-capsule approach. J Supercomput 81, 424 (2025). https://doi.org/10.1007/s11227-025-06941-4

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