Synthetic Aperture Radar (SAR) imagery is the primary data type used for sea
ice mapping due to its spatio-temporal coverage and the ability to detect sea
ice independent of cloud and lighting conditions. Automatic sea ice detection
using SAR imagery remains problematic due to the presence of ambiguous signal
and noise within the image. Conversely, ice and water are easily
distinguishable using multispectral imagery (MSI), but in the polar regions the
ocean's surface is often occluded by cloud or the sun may not appear above the
horizon for many months. To address some of these limitations, this paper
proposes a new tool trained using concurrent multispectral Visible and SAR
imagery for sea Ice Detection (ViSual\_IceD). ViSual\_IceD is a convolution
neural network (CNN) that builds on the classic U-Net architecture by
containing two parallel encoder stages, enabling the fusion and concatenation
of MSI and SAR imagery containing different spatial resolutions. The
performance of ViSual\_IceD is compared with U-Net models trained using
concatenated MSI and SAR imagery as well as models trained exclusively on MSI
or SAR imagery. ViSual\_IceD outperforms the other networks, with a F1 score
1.60\% points higher than the next best network, and results indicate that
ViSual\_IceD is selective in the image type it uses during image segmentation.
Outputs from ViSual\_IceD are compared to sea ice concentration products
derived from the AMSR2 Passive Microwave (PMW) sensor. Results highlight how
ViSual\_IceD is a useful tool to use in conjunction with PMW data, particularly
in coastal regions. As the spatial-temporal coverage of MSI and SAR imagery
continues to increase, ViSual\_IceD provides a new opportunity for robust,
accurate sea ice coverage detection in polar regions.

该研究提出了一种新的工具 ViSual_IceD，它使用并行编码器级别，将多光谱图像和合成孔径雷达 (SAR) 图像进行融合和连接，以实现极地区域可靠准确的海冰覆盖检测。

利用同时多光谱和合成孔径雷达图像进行海冰检测

Sea ice detection using concurrent multispectral and synthetic aperture  radar imagery

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.

使用机器学习算法对海冰进行自动化定位和分割，通过使用带噪声的 Sentinel-1 SAR 影像，开发了高效、具有空间分辨率、维度保持能力，并且对噪声更加稳健的面向海冰映射的算法。