Synthetic aperture radar (SAR) sea ice imagery is a promising source of data for sea ice data assimilation. Classification of SAR sea ice imagery into ice and water is of particular relevance due to its relationship with ice concentration, a key variable in sea ice data assimilation systems. With increasing volumes of SAR data, automated methods to carry out these classifications are of particular importance. Although several automated approaches have been proposed, none look at the impact of including an estimate of uncertainty of the model parameters and input features on the classification output. This article uses an established database of SAR image features to train a multilayer perceptron (MLP) neural network to classify pixel locations as either ice, water, or unknown. The classification accuracies are benchmarked using a recently developed logistic regression approach for the same database. The two methods are found to be comparable. The MLP approach is then enhanced to allow uncertainty to be estimated at each pixel location. Following methods proposed in the deep learning community, two kinds of uncertainty are considered. The first, epistemic uncertainty, is that due to uncertainty in the MLP weights. The second kind of uncertainty, aleatoric uncertainty, is that which cannot be explained by the model, and is therefore associated with the input data. It is found that including these uncertainties in the MLP models reduces their accuracies slightly, but also reduces misclassification rates. This is of particular importance for data assimilation applications, where misclassifications could severely degrade the analysis.

中文翻译：

---

用于检测 SAR 图像中冰水的不确定性神经网络的评估

合成孔径雷达 (SAR) 海冰图像是海冰数据同化的有前途的数据来源。由于 SAR 海冰图像与冰浓度（海冰数据同化系统中的一个关键变量）的关系，因此将 SAR 海冰图像分类为冰和水特别重要。随着 SAR 数据量的增加，执行这些分类的自动化方法尤为重要。尽管已经提出了几种自动化方法，但没有一个考虑将模型参数和输入特征的不确定性估计对分类输出的影响。本文使用已建立的 SAR 图像特征数据库来训练多层感知器 (MLP) 神经网络，以将像素位置分类为冰、水或未知。分类精度使用最近开发的逻辑回归方法对同一数据库进行基准测试。发现这两种方法具有可比性。然后增强 MLP 方法以允许在每个像素位置估计不确定性。遵循深度学习社区中提出的方法，考虑了两种不确定性。第一个，认知不确定性，是由于 MLP 权重的不确定性。第二种不确定性，任意不确定性，是模型无法解释的不确定性，因此与输入数据相关。发现在 MLP 模型中包含这些不确定性会略微降低其准确性，但也会降低误分类率。这对于数据同化应用尤为重要，