Joint sparse representation (JSR) is a commonly used classifier that recognizes different objects with core features extracted from images. However, the generalization ability is weak for the traditional linear kernel, and the objects with similar feature values associated with different categories are not sufficiently distinguished especially for a hyperspectral image (HSI). In this article, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the $\beta$ -whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some subclassifiers, and the $\beta$ function is utilized to enhance the exploration phase of the whale optimization algorithm and obtain the optimal weight of subclassifiers. Experimental results indicate that the performance of the proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for all HSIs.

中文翻译：

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基于加权小波核联合稀疏表示集合和β-鲸鱼优化算法的高光谱图像分类方法

联合稀疏表示（JSR）是一种常用的分类器，用于识别具有从图像中提取的核心特征的不同对象。然而，对于传统的线性核，泛化能力很弱，并且具有与不同类别相关联的相似特征值的对象不能被充分区分，尤其是对于高光谱图像（HSI）。本文提出了一种基于权重小波核JSR集成模型和HSI的HSI分类技术$ \ beta $ 提出了一种Whale优化算法进行像素级分类，其中小波函数作为JSR的核心。此外，集成学习用于通过一些子分类器的综合决策来确定每个样本的类别标签，并且$ \ beta $函数用于增强鲸鱼优化算法的探索阶段，并获得子分类器的最佳权重。实验结果表明，所提出的HSI分类方法的性能优于其他新提出的和相应的方法，在一定程度上消除了误分类和分类噪声，所有HSI的总体分类准确率均达到95％。