NDLI: Minimum class variance broad learning system for hyperspectral image classification

Content Provider	IET Digital Library
Author	Chen, Peng
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.
Starting Page	3039
Ending Page	3045
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 13, Nov (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1200
Journal	IET Image Processing
Publisher Date	2020-06-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Classification Performance Model Computer Vision And Image Processing Technique Data Structures Earth Sciences Extracted Spectral–spatial Features Feature Extraction File Organisation Generalisation Ability Geography And Cartography Computing Geophysical Image Processing Global Data Structure Global Discriminative Structure Information HSI Classification Hyperspectral Datasets Hyperspectral Image Hyperspectral Image Classification Image Classification Image Recognition Image Representation Interclass Discriminant Information Intraclass Distribution Structure Knowledge Engineering Technique Learning in AI Machine Learning Method MCVBLS Method Minimum Class Variance BLS Minimum Class Variance Broad Learning System Spectral–spatial Feature Learning Spectral–spatial Representation
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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