Cloud coverage prediction with deep learning methods

In this work, cloud images classification and segmentation have been implemented by using Deep Learning techniques. All images utilized in this thesis were obtained from TUBITAK National Observatory Bakirliktepe Campus, Cukurova University Space Science and Solar Energy Research and Application Center, and Singapore Whole-sky Imaging CATegories database. The main goal of our study is to compare the traditional image processing results with the Deep learning techniques. Firstly, two classes' classification solutions have been used. Our goal was to separate the cloud images from the others. We highlighted on Convolutional Neural Network (CNN) as classification and on SoftmaxWithLoss for the prediction. During our training phase, 92% accuracy has been scored as after fine-tuning our model. Secondly, some image processing techniques have been used to detect and segment the cloud images. Some edge detectors and watershed techniques have been implemented. Thirdly, some segmentation solutions have been proposed. Fully Convolutional Network (FCN) and U-NET have been used in this thesis. Two different methods of Deep Learning have been proposed to segment the cloud images and then make the prediction. Using U-NET model for the segmentation, 87% as dice coefficient and 45% as loss have been scored during the testing step. Different learning techniques were implemented on FCN such as stochastic gradient descent, Adam's momentum technique and Nesterov's momentum technique. The highest segmentation accuracy on FCN has been 63.12% with Adam's momentum technique. Keywords: Deep Learning, Cloud segmentation, Convolutional Neural Networks, FCN, U-NET.