Density Peak Covariance Matrix for Feature Extraction of Hyperspectral Image

The clustering methods have a good application in many aspects, in which the density peak (DP) clustering can effectively cluster similar neighboring pixels so that the features can be extracted well for hyperspectral images (HSIs) classification. In this work, a DP based covariance matrix (DPCM) method is proposed for the feature extraction of HSIs, which not only can effectively extract features but also can reduce the within-class variations and the between-class interference. The proposed method consists of the following steps: First, maximum noise fraction is employed on the original HSI to reduce the computational complexity and eliminate noise. Second, the local densities of the sample are calculated by the DP clustering. Therefore, a reconstructed image can be obtained in which each pixel has a density feature vector. Then, the covariance matrix between each density pixel in the density map is calculated. Last, the extracted covariance matrices are fed back to the support vector machine based on the logarithm Euclidean kernel for label assignment. Experiments are conducted on the Indian pine data set, in which each of the five randomly selected marker data are selected as the training sample. The experimental results show that the method can effectively improve the classification accuracy and is superior to other classification methods.