Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features

Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method...
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Gespeichert in:

Autor*in:	Ran Jing [verfasserIn] Zhaoning Gong [verfasserIn] Hongliang Guan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 228070-228087
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:228070-228087

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3045740

Katalog-ID:	DOAJ062196014

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allfields	10.1109/ACCESS.2020.3045740 doi (DE-627)DOAJ062196014 (DE-599)DOAJe080e81047af44f38a1d114cbb900b51 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Jing verfasserin aut Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods. Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery Electrical engineering. Electronics. Nuclear engineering Zhaoning Gong verfasserin aut Hongliang Guan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 228070-228087 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:228070-228087 https://doi.org/10.1109/ACCESS.2020.3045740 kostenfrei https://doaj.org/article/e080e81047af44f38a1d114cbb900b51 kostenfrei https://ieeexplore.ieee.org/document/9298743/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 8 2020 228070-228087
spelling	10.1109/ACCESS.2020.3045740 doi (DE-627)DOAJ062196014 (DE-599)DOAJe080e81047af44f38a1d114cbb900b51 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Jing verfasserin aut Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods. Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery Electrical engineering. Electronics. Nuclear engineering Zhaoning Gong verfasserin aut Hongliang Guan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 228070-228087 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:228070-228087 https://doi.org/10.1109/ACCESS.2020.3045740 kostenfrei https://doaj.org/article/e080e81047af44f38a1d114cbb900b51 kostenfrei https://ieeexplore.ieee.org/document/9298743/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 8 2020 228070-228087
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allfieldsGer	10.1109/ACCESS.2020.3045740 doi (DE-627)DOAJ062196014 (DE-599)DOAJe080e81047af44f38a1d114cbb900b51 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Jing verfasserin aut Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods. Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery Electrical engineering. Electronics. Nuclear engineering Zhaoning Gong verfasserin aut Hongliang Guan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 228070-228087 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:228070-228087 https://doi.org/10.1109/ACCESS.2020.3045740 kostenfrei https://doaj.org/article/e080e81047af44f38a1d114cbb900b51 kostenfrei https://ieeexplore.ieee.org/document/9298743/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 8 2020 228070-228087
allfieldsSound	10.1109/ACCESS.2020.3045740 doi (DE-627)DOAJ062196014 (DE-599)DOAJe080e81047af44f38a1d114cbb900b51 DE-627 ger DE-627 rakwb eng TK1-9971 Ran Jing verfasserin aut Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods. Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery Electrical engineering. Electronics. Nuclear engineering Zhaoning Gong verfasserin aut Hongliang Guan verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 228070-228087 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:228070-228087 https://doi.org/10.1109/ACCESS.2020.3045740 kostenfrei https://doaj.org/article/e080e81047af44f38a1d114cbb900b51 kostenfrei https://ieeexplore.ieee.org/document/9298743/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700 AR 8 2020 228070-228087
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callnumber-first	T - Technology
author	Ran Jing
spellingShingle	Ran Jing misc TK1-9971 misc Change detection misc image segmentation misc multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) misc stacked convolutional auto-encoder (SCAE) misc VHR satellite imagery misc Electrical engineering. Electronics. Nuclear engineering Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features
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topic_title	TK1-9971 Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features Change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery
topic	misc TK1-9971 misc Change detection misc image segmentation misc multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) misc stacked convolutional auto-encoder (SCAE) misc VHR satellite imagery misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Change detection misc image segmentation misc multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) misc stacked convolutional auto-encoder (SCAE) misc VHR satellite imagery misc Electrical engineering. Electronics. Nuclear engineering
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title	Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features
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title_full	Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features
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title_sort	land cover change detection with vhr satellite imagery based on multi-scale slic-cnn and scae features
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title_auth	Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features
abstract	Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods.
abstractGer	Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods.
abstract_unstemmed	Change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate “from-to” change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions; CNN features have an effect on the consistency of change regions; and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods.
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 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_4700
title_short	Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features
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