Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order c...

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Autores principales:	Xiaodong Yu, Rui Ding, Jingbo Shao, Xiaohui Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hyperspectral remote sensing images feature representation nuclear norm contractive auto-encoder Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/230209c8db7e4450aaf0b59e755ca927
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id	oai:doaj.org-article:230209c8db7e4450aaf0b59e755ca927
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spelling	oai:doaj.org-article:230209c8db7e4450aaf0b59e755ca9272021-11-11T15:39:41ZHyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint10.3390/electronics102126672079-9292https://doaj.org/article/230209c8db7e4450aaf0b59e755ca9272021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2667https://doaj.org/toc/2079-9292Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.Xiaodong YuRui DingJingbo ShaoXiaohui LiMDPI AGarticlehyperspectral remote sensing imagesfeature representationnuclear normcontractive auto-encoderElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2667, p 2667 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hyperspectral remote sensing images feature representation nuclear norm contractive auto-encoder Electronics TK7800-8360
spellingShingle	hyperspectral remote sensing images feature representation nuclear norm contractive auto-encoder Electronics TK7800-8360 Xiaodong Yu Rui Ding Jingbo Shao Xiaohui Li Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
description	Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.
format	article
author	Xiaodong Yu Rui Ding Jingbo Shao Xiaohui Li
author_facet	Xiaodong Yu Rui Ding Jingbo Shao Xiaohui Li
author_sort	Xiaodong Yu
title	Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
title_short	Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
title_full	Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
title_fullStr	Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
title_full_unstemmed	Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint
title_sort	hyperspectral remote sensing image feature representation method based on cae-h with nuclear norm constraint
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/230209c8db7e4450aaf0b59e755ca927
work_keys_str_mv	AT xiaodongyu hyperspectralremotesensingimagefeaturerepresentationmethodbasedoncaehwithnuclearnormconstraint AT ruiding hyperspectralremotesensingimagefeaturerepresentationmethodbasedoncaehwithnuclearnormconstraint AT jingboshao hyperspectralremotesensingimagefeaturerepresentationmethodbasedoncaehwithnuclearnormconstraint AT xiaohuili hyperspectralremotesensingimagefeaturerepresentationmethodbasedoncaehwithnuclearnormconstraint
_version_	1718434524716597248

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

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