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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Density Peak Covariance Matrix for Feature Extraction of Hyperspectral Image
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Density Peak Covariance Matrix for Feature Extraction of Hyperspectral Image

机译：高光谱图像特征提取的密度峰值协方差矩阵

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摘要

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.
机译：聚类方法在许多方面具有良好的应用，其中密度峰值（DP）聚类可以有效地聚集相似的相邻像素，使得可以为高光谱图像（HSIS）分类提供良好的特征。在这项工作中，提出了一种基于DP的协方差矩阵（DPCM）方法，用于HSI的特征提取，这不仅可以有效地提取特征，而且还可以降低课堂内变化和课堂之间的干扰。该方法包括以下步骤：首先，在原始HSI上采用最大噪声分数，以降低计算复杂性并消除噪声。其次，样品的局部密度由DP聚类计算。因此，可以获得重建的图像，其中每个像素具有密度特征向量。然后，计算密度图中的每个密度像素之间的协方差矩阵。最后，基于标签分配的对数欧几里德内核，提取的协方差矩阵被反馈到支持向量机。实验在印度松树数据集上进行，其中五个随机选择的标记数据中的每一个被选择为训练样本。实验结果表明，该方法可以有效提高分类精度，优于其他分类方法。

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