Enhancing sea ice segmentation in Sentinel-1 images with atrous
  convolutions
Rafael Pires de Lima Behzad Vahedi Nick Hughes Andrew P. Barrett Walter Meier Morteza Karimzadeh
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, models trained with our
largest training set have weighted F1 scores all greater than 0.95 for January
and July test scenes. Specifically, the median weighted F1 score was 0.98,
indicating high performance for both months. By comparison, a competitive
baseline U-Net has a weighted average F1 score of ranging from 0.92 to 0.94
(median 0.93) for July, and 0.97 to 0.98 (median 0.97) for January. Multiclass
ice type classification is more challenging, and even though our models achieve
2% improvement in weighted F1 average compared to the baseline U-Net, test
weighted F1 is generally between 0.6 and 0.80. Our approach can efficiently
segment full SAR scenes in one run, is faster than the baseline U-Net, retains
spatial resolution and dimension, and is more robust against noise compared to
approaches that rely on patch classification.