Density Peak-Based Noisy Label Detection for Hyperspectral Image Classification

Mislabeled training samples may have a negative effect on the performance of hyperspectral image classification. In order to solve this problem, a new density peak (DP) clustering-based noisy label detection method is proposed, which consists of the following steps. First, the distances among the training samples of each class are calculated using four representative distance metrics, i.e., the Euclidean distance (ED), orthogonal projection divergence (OPD), spectral information divergence (SID), and correlation coefficient (CC). Then, the local density of each training sample can be obtained using the DP clustering algorithm. Finally, a local density-based decision function is used to detect the noisy labels. The effectiveness of the proposed method is evaluated using the support vector machines on several real hyperspectral data sets. Experimental results demonstrate that the proposed noisy label detection method indeed helps in improving the classification performance.