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Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image | IEEE Journals & Magazine | IEEE Xplore

Local Manifold-Based Sparse Discriminant Learning for Feature Extraction of Hyperspectral Image


Abstract:

Hyperspectral image (HSI) generally contains a complex manifold structure and strong sparse correlation in its nonlinear high-dimensional data space. However, the existin...Show More

Abstract:

Hyperspectral image (HSI) generally contains a complex manifold structure and strong sparse correlation in its nonlinear high-dimensional data space. However, the existing manifold learning and sparse learning methods usually consider the manifold structure and sparse relationship separately rather than combining manifold and sparse properties to discover the intrinsic information in the original data. To simultaneously reveal the complex sparse relation and manifold structure of HSI, a novel feature extraction (FE) method, called local manifold-based sparse discriminant learning (LMSDL), has been proposed on the basis of manifold learning and sparse representation (SR). The LMSDL method first designs a new sparse optimization model called local manifold-based SR (LMSR) to reveal the local manifold-based sparse structure of data. Then, two geometrical sparse graphs are constructed to represent the discriminant relationship between samples and the geometrical and sparse neighbors. An objective function is constructed via geometrical sparse graphs and reconstruction points to learn a projection matrix for FE. The LMSDL effectively reveals the complex sparse relation and manifold structure in high-dimensional data, and it enhances the representation ability of extracted features for HSI classification significantly. The experimental results on the three real HSI datasets show that the proposed LMSDL algorithm possesses better performance in comparison with some state-of-the-art FE methods.
Published in: IEEE Transactions on Cybernetics ( Volume: 51, Issue: 8, August 2021)
Page(s): 4021 - 4034
Date of Publication: 20 March 2020

ISSN Information:

PubMed ID: 32203046

Funding Agency:

Author image of Yule Duan
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Yule Duan received the B.S. degree in measuring and testing technology and instruments from Tianjin Polytechnic University, Tianjin, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are pattern recognition, machine learning, and image processing in general.
Yule Duan received the B.S. degree in measuring and testing technology and instruments from Tianjin Polytechnic University, Tianjin, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are pattern recognition, machine learning, and image processing in general.View more
Author image of Hong Huang
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Hong Huang (Member, IEEE) received the M.S. degree in pattern recognition and the Ph.D. degree in instrument science and technology from Chongqing University, Chongqing, China, in 2005 and 2008, respectively.
He is currently a Professor with the Image Information Processing Laboratory, Chongqing University and a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Is...Show More
Hong Huang (Member, IEEE) received the M.S. degree in pattern recognition and the Ph.D. degree in instrument science and technology from Chongqing University, Chongqing, China, in 2005 and 2008, respectively.
He is currently a Professor with the Image Information Processing Laboratory, Chongqing University and a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Is...View more
Author image of Zhengying Li
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Zhengying Li received the B.S. degree in measurement and control technology and instrument from the North University of China, Taiyuan, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests include image processing, hyperspectral image classification, machine vision, and target tracking.
Zhengying Li received the B.S. degree in measurement and control technology and instrument from the North University of China, Taiyuan, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests include image processing, hyperspectral image classification, machine vision, and target tracking.View more
Author image of Yuxiao Tang
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Yuxiao Tang received the B.S. degree in instrument science and technology from the Hefei University of Technology, Hefei, China, in 2017. He is currently pursuing the master’s degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are hyperspectral image feature extraction, image processing, and face recognition.
Yuxiao Tang received the B.S. degree in instrument science and technology from the Hefei University of Technology, Hefei, China, in 2017. He is currently pursuing the master’s degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are hyperspectral image feature extraction, image processing, and face recognition.View more

I. Introduction

Hyperspectral image (HSI) captured by the hyperspectral remote sensor records the electromagnetic wave of the Earth’s surface, which contains dozens or even hundreds of continuous spectral bands from the visible to near-infrared spectrum region [1], [2]. The HSI provides rich spectral information for identifying ground objects, but the strong correlations among spectral bands usually result in huge redundant data that consume high computing and storage resources [3]. In addition, the high-dimensional characteristic of HSI causes the Hughes phenomenon that the performance of HSI classification declines as the dimensionality increases, especially when only limited training samples are available [4], [5]. Therefore, it is an urgent issue to significantly reduce the dimensionality of HSI without any appreciable loss of useful information [6].

Author image of Yule Duan
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Yule Duan received the B.S. degree in measuring and testing technology and instruments from Tianjin Polytechnic University, Tianjin, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are pattern recognition, machine learning, and image processing in general.
Yule Duan received the B.S. degree in measuring and testing technology and instruments from Tianjin Polytechnic University, Tianjin, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are pattern recognition, machine learning, and image processing in general.View more
Author image of Hong Huang
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Hong Huang (Member, IEEE) received the M.S. degree in pattern recognition and the Ph.D. degree in instrument science and technology from Chongqing University, Chongqing, China, in 2005 and 2008, respectively.
He is currently a Professor with the Image Information Processing Laboratory, Chongqing University and a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, South Kingstown, RI, USA. His main research activities are in the fields of image processing, pattern recognition, and remote sensing. In particular, he pays more attention to sparse representation, manifold learning, and hyperspectral remote sensing.
Prof. Huang is a Reviewer of over 20 international journals, including the IEEE Transactions on Geoscience and Remote Sensing, the ISPRS Journal of Photogrammetry and Remote Sensing, the IEEE Transactions on Medical Imaging, Neurocomputing, and Information Sciences.
Hong Huang (Member, IEEE) received the M.S. degree in pattern recognition and the Ph.D. degree in instrument science and technology from Chongqing University, Chongqing, China, in 2005 and 2008, respectively.
He is currently a Professor with the Image Information Processing Laboratory, Chongqing University and a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, South Kingstown, RI, USA. His main research activities are in the fields of image processing, pattern recognition, and remote sensing. In particular, he pays more attention to sparse representation, manifold learning, and hyperspectral remote sensing.
Prof. Huang is a Reviewer of over 20 international journals, including the IEEE Transactions on Geoscience and Remote Sensing, the ISPRS Journal of Photogrammetry and Remote Sensing, the IEEE Transactions on Medical Imaging, Neurocomputing, and Information Sciences.View more
Author image of Zhengying Li
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Zhengying Li received the B.S. degree in measurement and control technology and instrument from the North University of China, Taiyuan, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests include image processing, hyperspectral image classification, machine vision, and target tracking.
Zhengying Li received the B.S. degree in measurement and control technology and instrument from the North University of China, Taiyuan, China, in 2016. He is currently pursuing the Ph.D. degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests include image processing, hyperspectral image classification, machine vision, and target tracking.View more
Author image of Yuxiao Tang
Key Laboratory of Optoelectronic Technology and Systems, Education Ministry of China, Chongqing University, Chongqing, China
Yuxiao Tang received the B.S. degree in instrument science and technology from the Hefei University of Technology, Hefei, China, in 2017. He is currently pursuing the master’s degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are hyperspectral image feature extraction, image processing, and face recognition.
Yuxiao Tang received the B.S. degree in instrument science and technology from the Hefei University of Technology, Hefei, China, in 2017. He is currently pursuing the master’s degree in instrument science and technology with Chongqing University, Chongqing, China.
His research interests are hyperspectral image feature extraction, image processing, and face recognition.View more

References

References is not available for this document.