Comparison of Deep Learning Models for the Classification of Noctilucent Cloud Images

• Published in: Remote Sensing
• Main Authors: Rajendra Sapkota, Puneet Sharma, Ingrid Mann
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: noctilucent cloud (NLC); machine learning; convolutional neural network; transfer learning; image classification; saliency map; Science; Q; Arctic
• Online Access: https://doi.org/10.3390/rs14102306; https://doaj.org/article/8282ccf85c3c431ea2b3b6504d8ab49b

Optically thin layers of tiny ice particles near the summer mesopause, known as noctilucent clouds, are of significant interest within the aeronomy and climate science communities. Ground-based optical cameras mounted at various locations in the arctic regions collect the dataset during favorable summer times. In this paper, first, we compare the performances of various deep learning-based image classifiers against a baseline machine learning model trained with support vector machine (SVM) algorithm to identify an effective and lightweight model for the classification of noctilucent clouds. The SVM classifier is trained with histogram of oriented gradient (HOG) features, and deep learning models such as SqueezeNet, ShuffleNet, MobileNet, and Resnet are fine-tuned based on the dataset. The dataset includes images observed from different locations in northern Europe with varied weather conditions. Second, we investigate the most informative pixels for the classification decision on test images. The pixel-level attributions calculated using the guide back-propagation algorithm are visualized as saliency maps. Our results indicate that the SqueezeNet model achieves an F1 score of 0.95. In addition, SqueezeNet is the lightest model used in our experiments, and the saliency maps obtained for a set of test images correspond better with relevant regions associated with noctilucent clouds.