Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data

To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract s...
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Autor*in:	Tong Gao [verfasserIn] Hao Chen [verfasserIn] Junhong Lu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Tucker decomposition coupled heterogeneous Tucker decomposition multisource heterogeneous data fusion heterogeneous domain adaptation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 14(2022), 11, p 2553
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:11, p 2553

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs14112553

Katalog-ID:	DOAJ041537025

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spelling	10.3390/rs14112553 doi (DE-627)DOAJ041537025 (DE-599)DOAJ1ecd358612cf43d59eeb27b7f771fbb8 DE-627 ger DE-627 rakwb eng Tong Gao verfasserin aut Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods. Tucker decomposition coupled heterogeneous Tucker decomposition multisource heterogeneous data fusion heterogeneous domain adaptation Science Q Hao Chen verfasserin aut Junhong Lu verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2553 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2553 https://doi.org/10.3390/rs14112553 kostenfrei https://doaj.org/article/1ecd358612cf43d59eeb27b7f771fbb8 kostenfrei https://www.mdpi.com/2072-4292/14/11/2553 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 11, p 2553
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allfieldsSound	10.3390/rs14112553 doi (DE-627)DOAJ041537025 (DE-599)DOAJ1ecd358612cf43d59eeb27b7f771fbb8 DE-627 ger DE-627 rakwb eng Tong Gao verfasserin aut Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods. Tucker decomposition coupled heterogeneous Tucker decomposition multisource heterogeneous data fusion heterogeneous domain adaptation Science Q Hao Chen verfasserin aut Junhong Lu verfasserin aut In Remote Sensing MDPI AG, 2009 14(2022), 11, p 2553 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:14 year:2022 number:11, p 2553 https://doi.org/10.3390/rs14112553 kostenfrei https://doaj.org/article/1ecd358612cf43d59eeb27b7f771fbb8 kostenfrei https://www.mdpi.com/2072-4292/14/11/2553 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 14 2022 11, p 2553
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author	Tong Gao
spellingShingle	Tong Gao misc Tucker decomposition misc coupled heterogeneous Tucker decomposition misc multisource heterogeneous data fusion misc heterogeneous domain adaptation misc Science misc Q Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
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topic_title	Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data Tucker decomposition coupled heterogeneous Tucker decomposition multisource heterogeneous data fusion heterogeneous domain adaptation
topic	misc Tucker decomposition misc coupled heterogeneous Tucker decomposition misc multisource heterogeneous data fusion misc heterogeneous domain adaptation misc Science misc Q
topic_unstemmed	misc Tucker decomposition misc coupled heterogeneous Tucker decomposition misc multisource heterogeneous data fusion misc heterogeneous domain adaptation misc Science misc Q
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title	Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
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title_full	Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
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title_sort	coupled heterogeneous tucker decomposition: a feature extraction method for multisource fusion and domain adaptation using multisource heterogeneous remote sensing data
title_auth	Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
abstract	To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods.
abstractGer	To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods.
abstract_unstemmed	To excavate adequately the rich information contained in multisource remote sensing data, feature extraction as basic yet important research has two typical applications: one of which is to extract complementary information of multisource data to improve classification; and the other is to extract shared information across sources for domain adaptation. However, typical feature extraction methods require the input represented as vectors or homogeneous tensors and fail to process multisource data represented as heterogeneous tensors. Therefore, the coupled heterogeneous Tucker decomposition (C-HTD) containing two sub-methods, namely coupled factor matrix-based HTD (CFM-HTD) and coupled core tensor-based HTD (CCT-HTD), is proposed to establish a unified feature extraction framework for multisource fusion and domain adaptation. To handle multisource heterogeneous tensors, multiple Tucker models were constructed to extract features of different sources separately. To cope with the supervised and semi-supervised cases, the class-indicator factor matrix was built to enhance the separability of features using known labels and learned labels. To mine the complementarity of paired multisource samples, coupling constraint was imposed on multiple factor matrices to form CFM-HTD to extract multisource information jointly. To extract domain-adapted features, coupling constraint was imposed on multiple core tensors to form CCT-HTD to encourage data from different sources to have the same class centroid. In addition, to reduce the impact of interference samples on domain adaptation, an adaptive sample-weighting matrix was designed to autonomously remove outliers. Using multiresolution multiangle optical and MSTAR datasets, experimental results show that the C-HTD outperforms typical multisource fusion and domain adaptation methods.
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title_short	Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data
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up_date	2024-07-03T20:46:55.775Z
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Coupled Heterogeneous Tucker Decomposition: A Feature Extraction Method for Multisource Fusion and Domain Adaptation Using Multisource Heterogeneous Remote Sensing Data

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