Machine learning techniques, and specifically neural networks, have proved to be very useful tools for image classification tasks. Nevertheless, measuring the reliability of these networks and calibrating them accurately are very complex. This is even more complex in a field like hyperspectral imaging, where labeled data are scarce and difficult to generate. Bayesian neural networks (BNNs) allow to obtain uncertainty metrics related to the data processed (aleatoric), and to the uncertainty generated by the model selected (epistemic). On this work, we will demonstrate the utility of BNNs by analyzing the uncertainty metrics obtained by a BNN over five of the most used hyperspectral images datasets. In addition, we will illustrate how these metrics can be used for several practical applications such as identifying predictions that do not reach the required level of accuracy, detecting mislabeling in the dataset, or identifying when the predictions are affected by the increase of the level of noise in the input data.

中文翻译：

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贝叶斯神经网络使用不确定性指标分析高光谱数据集


机器学习技术，特别是神经网络，已被证明是图像分类任务非常有用的工具。然而，测量这些网络的可靠性并对其进行准确校准非常复杂。在高光谱成像等标记数据稀缺且难以生成的领域中，这一点甚至更加复杂。贝叶斯神经网络 (BNN) 允许获取与处理的数据（任意）以及所选模型生成的不确定性（认知）相关的不确定性度量。在这项工作中，我们将通过分析 BNN 在五个最常用的高光谱图像数据集上获得的不确定性度量来展示 BNN 的实用性。此外，我们将说明如何将这些指标用于多种实际应用，例如识别未达到所需准确度水平的预测、检测数据集中的错误标签，或者识别预测何时受到预测水平增加的影响。输入数据中的噪声。