Deep neural networks have established as a powerful tool for large scale supervised classification tasks. The state-of-the-art performances of deep neural networks are conditioned to the availability of large number of accurately labeled samples. In practice, collecting large scale accurately labeled datasets is a challenging and tedious task in most scenarios of remote sensing image analysis, thus cheap surrogate procedures are employed to label the dataset. Training deep neural networks on such datasets with inaccurate labels easily overfits to the noisy training labels and degrades the performance of the classification tasks drastically. To mitigate this effect, we propose an original solution with entropic optimal transportation. It allows to learn in an end-to-end fashion deep neural networks that are, to some extent, robust to inaccurately labeled samples. We empirically demonstrate on several remote sensing datasets, where both scene and pixel-based hyperspectral images are considered for classification. Our method proves to be highly tolerant to significant amounts of label noise and achieves favorable results against state-of-the-art methods.

深度神经网络已建立为大型监督分类任务的强大工具。深度神经网络的最新性能取决于大量准确标记的样本的可用性。在实践中，在大多数遥感影像分析场景中，收集大规模的，准确标记的数据集是一项艰巨而繁琐的任务，因此，采用廉价的替代程序来标记数据集。在这样的数据集上使用不正确的标签来训练深度神经网络很容易过分适应嘈杂的训练标签，并且会大大降低分类任务的性能。为了减轻这种影响，我们提出了带有熵最优运输的原始解决方案。它允许以端到端的方式学习深度神经网络，该神经网络在某种程度上是 对标记不正确的样品具有鲁棒性。我们在几个遥感数据集上进行了经验论证，其中考虑了基于场景和基于像素的高光谱图像进行分类。事实证明，我们的方法对大量标签噪声具有高度的耐受性，并且与最新技术方法相比，可以获得令人满意的结果。