Using Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoes

• Published in: Remote Sensing
• Main Authors: Erik Seip Domben, Puneet Sharma, Ingrid Mann
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: polar mesospheric summer echoes; deep learning; segmentation; Science; Q; Arctic; EISCAT
• Online Access: https://doi.org/10.3390/rs15174291; https://doaj.org/article/9297871a6dfd445fb9e0a5957fca2f50

Polar mesospheric summer echoes (PMSE) are radar echoes that are observed in the mesosphere during the arctic summer months in the polar regions. By studying PMSE, researchers can gain insights into physical and chemical processes that occur in the upper atmosphere—specifically, in the 80 to 90 km altitude range. In this paper, we employ fully convolutional networks such as UNET and UNET++ for the purpose of segmenting PMSE from the EISCAT VHF dataset. First, experiments are performed to find suitable weights and hyperparameters for UNET and UNET++. Second, different loss functions are tested to find one suitable for our task. Third, as the number of PMSE samples used is relatively small, this can lead to poor generalization. To address this, image-level and object-level augmentation methods are employed. Fourth, we briefly explain our findings by employing layerwise relevance propagation.