Modified-mean-shift-based noisy label detection for hyperspectral image classification

Abstract

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.