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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >River Ice Segmentation With Deep Learning
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River Ice Segmentation With Deep Learning

机译：河冰分割与深度学习

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摘要

This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain.
机译：本文涉及从冻结期间获得的数字图像计算两种河冰的表面浓度问题。它提出了尝试使用基于深度卷积神经网络（CNN）的若干先进的语义分割方法来解决这个问题的结果。这项任务提出了两个主要挑战 - 非常有限的培训数据和存在嘈杂标签的存在，这是由于两种类型的冰甚至是人类专家的视觉区分。结果用于分析目前存在的一些最佳深度学习方法的程度可以处理这些挑战。实验中使用的代码和数据公开可用于促进该领域的进一步工作。

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