A new machine learning method named as a broad learning system (BLS) has been proposed recently. The advantage of simple, fast, and good generalisation ability make it attracting extensive attention. In this study, by introducing BLS to solving hyperspectral image (HSI) classification, a minimum class variance BLS (MCVBLS) was proposed. Firstly, in order to get spectral–spatial representation of original HSI, spectral–spatial feature learning has been performed to take full advantage of abundant spectral and spatial information of HSI. Then, the authors use MCVBLS to classify the extracted spectral–spatial features. MCVBLS, in contrast to BLS, fully considers the global data structure and discriminant information of the data. MCVBLS enhances the classification performance model by minimising the intra-class distribution structure while maximising the inter-class discriminant information, the measure of placing restrictions on output weights will take more discriminative information and global discriminative structure information into consideration. Conducting an experiment on three benchmark hyperspectral datasets, they demonstrate that the proposed MCVBLS methods are effective for HSI classification, better than other state-of-the-art methods.

中文翻译：

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用于高光谱图像分类的最小类别方差广义学习系统

最近已经提出了一种新的机器学习方法，称为广泛学习系统（BLS）。简单，快速和良好的泛化能力的优点使其受到广泛关注。在这项研究中，通过将BLS引入求解高光谱图像（HSI）分类，提出了最小类别方差BLS（MCVBLS）。首先，为了获得原始HSI的光谱空间表示，已经进行了光谱空间特征学习以充分利用HSI丰富的光谱和空间信息。然后，作者使用MCVBLS对提取的光谱空间特征进行分类。与BLS相比，MCVBLS充分考虑了全局数据结构和数据的判别信息。MCVBLS通过最小化类内分布结构同时最大化类间判别信息来增强分类性能模型，对输出权重进行限制的措施将考虑更多的判别信息和全局判别结构信息。通过对三个基准高光谱数据集进行实验，他们证明了所提出的MCVBLS方法对HSI分类有效，优于其他最新方法。