Deep Learning Strategies for Analysis of Weather-Degraded Optical Sea Ice Images

Ship-based sea ice analysis algorithms rely on optical images captured in optimal weather conditions with high visibility. However, Arctic imagery is often affected by weather-related degradation due to haze, snow, and rain, impacting the efficacy of deep learning tasks for sea ice analysis, such as segmentation and classification. This article introduces and evaluates two strategies to address weather-induced degradation in optical sea ice images (RGB). Strategy 1 employs a two-step pipeline: first, removal of weather degradation using deep learning-based de-weathering algorithms, and then, analysis of images as a part of sea ice segmentation/classification tasks. Strategy 2 proposes a “weather as augmentation” training approach to create all-in-one weather-resilient segmentation and classification models. Furthermore, we introduce the first open-source ice image dataset (SeaIceWeather) with paired images—one clean and one weather-degraded. Such a dataset allows for training and validation of supervised deep learning-based de-weathering algorithms. Using this dataset, we show that the proposed strategies are effective against weather-degraded images, achieving parity with the segmentation and classification performance on clean images. In addition, we demonstrate de-weathering models capable of removing degradations due to four different weather conditions, including rain, haze, snow, and raindrops on a camera lens with a single set of weights. The presented strategies lay the foundation for robust shipborne sea ice analysis systems resilient to adverse weather conditions. Furthermore, we hope that the dataset introduced in this study ignites further interest in the analysis of weather-degraded sea ice images. The SeaIceWeather dataset can be downloaded from IEEE DataPort (
https://ieee-dataport.org/documents/seaiceweather
).