Superpixel Correction Based Label Propagation for Hyperspectral Images Classification

Superpixel based label propagation models have been successfully used for Hyperspectral Images (HSIs) classification especially when the training data are limited. However, it is inevitable that there are segmentation errors leading to data in one superpixel containing samples from different classes which could decrease the classification accuracy. In order to address this issue, we propose Superpixel Correction based Label Propagation for HSIs classification. First, superpixel segmentation technique is adopted to segment a HSI into many superpixel blocks. Then, clustering model density peak is used to adaptively cluster the data in each superpixel block to correct the segmentation errors. Finally, based on the corrected superpixel segmentation we construct global-local and spatial-spectral similarity graphs which results into effective propagation matrix for label propagation. The proposed model is verified in two HSIs data sets, and the experimental results demonstrate that the proposed model is superior to several state-of-the-art methods in terms of classification accuracy, especially in the case of limited training samples.