Minimum class variance broad learning system for hyperspectral image classification

Abstract

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.