Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions ...

• Main Authors: de Lima, Rafael Pires, Vahedi, Behzad, Hughes, Nick, Barrett, Andrew P., Meier, Walter, Karimzadeh, Morteza
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: arXiv 2023
• Subjects: Image and Video Processing eess.IV; Computer Vision and Pattern Recognition cs.CV; FOS Electrical engineering, electronic engineering, information engineering; FOS Computer and information sciences; Pyramid; Sea ice
• Online Access: https://dx.doi.org/10.48550/arxiv.2310.17122; https://arxiv.org/abs/2310.17122

Due to the growing volume of remote sensing data and the low latency required for safe marine navigation, machine learning (ML) algorithms are being developed to accelerate sea ice chart generation, currently a manual interpretation task. However, the low signal-to-noise ratio of the freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery, the ambiguity of backscatter signals for ice types, and the scarcity of open-source high-resolution labelled data makes automating sea ice mapping challenging. We use Extreme Earth version 2, a high-resolution benchmark dataset generated for ML training and evaluation, to investigate the effectiveness of ML for automated sea ice mapping. Our customized pipeline combines ResNets and Atrous Spatial Pyramid Pooling for SAR image segmentation. We investigate the performance of our model for: i) binary classification of sea ice and open water in a segmentation framework; and ii) a multiclass segmentation of five sea ice types. For binary ice-water classification, ...