Open AccessMinimum class variance broad learning system for hyperspectral image classification
Author(s) -
Chen Peng
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.1200
Subject(s) - hyperspectral imaging , discriminative model , pattern recognition (psychology) , artificial intelligence , discriminant , computer science , spatial analysis , feature (linguistics) , linear discriminant analysis , class (philosophy) , benchmark (surveying) , mathematics , contextual image classification , machine learning , image (mathematics) , statistics , geography , linguistics , philosophy , geodesy
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.