Euclidean distance-based adaptive collaborative representation with Tikhonov regularization for hyperspectral image classification

文献类型： 外文期刊

第一作者： Yang, Rongchao

作者： Yang, Rongchao;Kan, Jiangming;Yang, Rongchao;Kan, Jiangming;Yang, Rongchao

作者机构：

关键词： Hyperspectral image (HSI) classification; Collaborative representation (CR); Tikhonov regularization; Euclidean distance-based adaptive (EDA) dictionary learning

期刊名称：MULTIMEDIA TOOLS AND APPLICATIONS （ 影响因子：2.577； 五年影响因子：2.396 ）

ISSN： 1380-7501

年卷期：

页码：

收录情况： SCI

摘要： In recent years, collaborative representation (CR)-based models have been widely used in hyperspectral image (HSI) classification. And many CR models utilize Tikhonov regularization term to improve the classification performance. However, this regularization term greatly increases the running time of CR models due to calculating the Euclidean distance between the test sample and all the training samples. Moreover, most of CR models utilize the training samples of all classes to represent the test sample, which may make the classes irrelevant to the test sample produce an impact on the classification performance of CR. To solve the above problems and further improve the classification performance of the CR model, a novel Euclidean distance-based adaptive (EDA) dictionary learning method is proposed in this paper, which aims to construct a subdictionary by selecting the k-nearest neighbor classes relevant to the test sample using Euclidean distance. And the EDA method is integrated into the original collaborative representation classifier (CRC) and the CRC with Tikhonov regularization (CRT) methods for HSI classification, which is denoted as EDACRC and EDACRT, respectively. The proposed methods are evaluated on the Botswana, KSC, and Salinas-A datasets, in which the classification performances and running times of these methods are analyzed and compared. The experimental results demonstrate that the proposed EDA method can not only effectively reduce the running time of the CR model with Tikhonov regularization, but also effectively eliminate some classes irrelevant to the test sample, thus improving the classification performance of the CR model.

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