Labeling mistakes occur in the many real training sets of hyperspectral image (HSI) classification, due to mistakes in the collection of labeled training data samples phase. Label noise is an essential problem in classification, with many possible negative consequences. In this paper, a new method is proposed to detect and delete noisy label samples and therefore remove the effect of errors in the classification process. For this purpose, first, a modified mean shift (MMS) method originated from minimum Bayesian risk is proposed and used to improve the separability of the mislabeled samples from training sets. Then denoised training samples are given to the classification algorithms such as SVM, KNN, MLR, and KOMP to classify the HSI data sets. Then, the effect of several types of loss functions is investigated for the proposed MMS method. Also, the analysis of bounded differences inequality, mean square error (MSE), asymptotic mean square error (AMSE), and asymptotic normality are obtained for our method. The performance of the mentioned compared methods greatly improves at high levels of noise, when the MMS method are combined to them. The obtained experimental results show that the proposed MMS method improved the performance of these classification methods for real HSI data sets.

由于标记训练数据样本收集阶段的错误，在高光谱图像（HSI）分类的许多真实训练集中都会发生标记错误。标签噪声是分类中的一个基本问题，具有许多可能的负面后果。在本文中，提出了一种新的方法来检测和删除噪声标签样本，从而消除分类过程中错误的影响。为此，首先，提出了一种源自最小贝叶斯风险的修正均值漂移 (MMS) 方法，并用于提高错误标记样本与训练集的可分离性。然后将去噪后的训练样本交给SVM、KNN、MLR、KOMP等分类算法对HSI数据集进行分类。然后，针对所提出的 MMS 方法研究了几种类型的损失函数的影响。还，我们的方法获得了对有界差异不等式、均方误差（MSE）、渐近均方误差（AMSE）和渐近正态性的分析。当 MMS 方法与它们相结合时，上述比较方法的性能在高噪声水平下大大提高。获得的实验结果表明，所提出的 MMS 方法提高了这些分类方法对真实 HSI 数据集的性能。