Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN)


  • Sin Liang Lim Multimedia University
  • Pei Yong Lo


Atmospheric cloud image detection, convolutional neural network (CNN), transfer learning, U-Net, ResNet34, VGG16


Cloud is an aerosol consisting of visible mass of miniature liquid droplets, frozen crystal, or other particles suspended in the atmosphere. The study of atmospheric clouds is crucial for us to better understand and predict the behaviors of clouds, which has implications for climate, weather, aviation safety, agriculture, and energy production. Convolutional neural network (CNN) method is applied to train an atmospheric cloud image detection model to identify the presence of cloud and classify them. Supervised learning method is applied to train the model such that the machine is given labeled cloud image dataset to learn how to classify and predict the presence of cloud. U-Net architecture is used to train the atmospheric cloud image detection model because the architecture has the highest performance in image segmentation especially object detection in satellite images. The 38-Cloud Dataset which is used to train the model, is obtained from Landsat 8 Earth observation satellite. The dataset is randomly divided into training set (75% of the total images) and validation set (25% of the total images). Following this, the dataset is preprocessed and transformed into tensors to train the model. The training has been carried out for 50 epochs. Apart from the U-Net architecture proposed, the architecture is further modified with ResNet34 and VGG16 and the performance of each model is studied. The recognition accuracy obtained for atmospheric cloud image detection trained with the dataset achieved 97%. With this accuracy, U-Net architecture can be justified as a powerful and suitable convolutional neural network in performing atmospheric cloud image detection. 


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Author Biography

  • Sin Liang Lim, Multimedia University

    Dr Lim Sin Liang received her Bachelor of Engineering, Master in Engineering, and PhD degrees from Multimedia University, RMIT (Royal Melbourne Institute of Technology) University, Australia, and Multimedia University, Malaysia in the years of 2001, 2003, and 2012 respectively. She joined Multimedia University as a Lecturer in 2003, and she is currently a Senior Lecturer in Faculty of Engineering, Cyberjaya campus in Multimedia University, Malaysia. With her PhD thesis entitled "Derivation of Novel Quantitative Fields via Mathematical Morphology", her research interests include terrestrial pattern retrieval using mathematical morphology, convexity measure and geodesic spectrum in digital topographic basins, and cloud field segmentation via multiscale convexity analysis.




How to Cite

Lim, S. L., & Lo, P. Y. (2024). Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN). International Journal of Integrated Engineering, 16(3), 145-156.