Atmospheric Cloud Image Detection with Convolutional Neural Network (CNN) - Lim, Sin Liang; Lo, Pei Yong (International Journal of Integrated Engineering, 2024)
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.