Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images

High-spatial resolution urban scenes, which contain ecological, social, and environmental zones, are composed of diverse functional areas. Depicting urban scenes from both qualitative and quantitative aspects, i.e., scene classification and scene unmixing, are increasingly important tasks. The purpo...

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Autores principales:	Qiqi Zhu, Jiale Chen, Linlin Wang, Qingfeng Guan
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	High-resolution imagery nonnegative matrix factorization (NMF) remote sensing scene unmixing urban mixed scene (UMS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
Acceso en línea:	https://doaj.org/article/f222f83121c247d38e84f1b4eba40b4e
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id	oai:doaj.org-article:f222f83121c247d38e84f1b4eba40b4e
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spelling	oai:doaj.org-article:f222f83121c247d38e84f1b4eba40b4e2021-11-17T00:00:10ZProportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images2151-153510.1109/JSTARS.2021.3119988https://doaj.org/article/f222f83121c247d38e84f1b4eba40b4e2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9573506/https://doaj.org/toc/2151-1535High-spatial resolution urban scenes, which contain ecological, social, and environmental zones, are composed of diverse functional areas. Depicting urban scenes from both qualitative and quantitative aspects, i.e., scene classification and scene unmixing, are increasingly important tasks. The purpose of scene classification is to mark a scene as a separate category, and this approach is widely applied to qualitatively define scenes. However, with the rapid development of cities, urban scenes usually show a mixed nature, resulting in urban mixed scenes (UMSs). Traditional scene classification cannot describe the mix of urban scenes. Few studies have focused on the quantitative measurements of UMSs, which aim at discovering and analyzing the proportion of different subscenes. In this article, a scene unmixing framework based on nonnegative matrix factorization for UMSs (UnUMS) is proposed to estimate the mixing ratio of urban scenes. In the UnUMS, a deep feature representation is used for the high-dimensional representation of the image, and then a nonnegative matrix factorization strategy is used to estimate the proportion of UMSs. Finally, the UMS can be described by the proportion of each subscene. To confirm the robust generalizability of UnUMS, the WH-center Mix dataset with a set of small mixed scenes and the WH-hy LMix dataset with a large UMS image are utilized. The root-mean-square error and the newly proposed vector angular distance are utilized to quantitatively evaluate the effect of scene unmixing from different aspects. Experiments with the two datasets demonstrate that UnUMS can accurately estimate the proportions of different scenes for UMS.Qiqi ZhuJiale ChenLinlin WangQingfeng GuanIEEEarticleHigh-resolution imagerynonnegative matrix factorization (NMF)remote sensingscene unmixingurban mixed scene (UMS)Ocean engineeringTC1501-1800Geophysics. Cosmic physicsQC801-809ENIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11257-11270 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	High-resolution imagery nonnegative matrix factorization (NMF) remote sensing scene unmixing urban mixed scene (UMS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809
spellingShingle	High-resolution imagery nonnegative matrix factorization (NMF) remote sensing scene unmixing urban mixed scene (UMS) Ocean engineering TC1501-1800 Geophysics. Cosmic physics QC801-809 Qiqi Zhu Jiale Chen Linlin Wang Qingfeng Guan Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
description	High-spatial resolution urban scenes, which contain ecological, social, and environmental zones, are composed of diverse functional areas. Depicting urban scenes from both qualitative and quantitative aspects, i.e., scene classification and scene unmixing, are increasingly important tasks. The purpose of scene classification is to mark a scene as a separate category, and this approach is widely applied to qualitatively define scenes. However, with the rapid development of cities, urban scenes usually show a mixed nature, resulting in urban mixed scenes (UMSs). Traditional scene classification cannot describe the mix of urban scenes. Few studies have focused on the quantitative measurements of UMSs, which aim at discovering and analyzing the proportion of different subscenes. In this article, a scene unmixing framework based on nonnegative matrix factorization for UMSs (UnUMS) is proposed to estimate the mixing ratio of urban scenes. In the UnUMS, a deep feature representation is used for the high-dimensional representation of the image, and then a nonnegative matrix factorization strategy is used to estimate the proportion of UMSs. Finally, the UMS can be described by the proportion of each subscene. To confirm the robust generalizability of UnUMS, the WH-center Mix dataset with a set of small mixed scenes and the WH-hy LMix dataset with a large UMS image are utilized. The root-mean-square error and the newly proposed vector angular distance are utilized to quantitatively evaluate the effect of scene unmixing from different aspects. Experiments with the two datasets demonstrate that UnUMS can accurately estimate the proportions of different scenes for UMS.
format	article
author	Qiqi Zhu Jiale Chen Linlin Wang Qingfeng Guan
author_facet	Qiqi Zhu Jiale Chen Linlin Wang Qingfeng Guan
author_sort	Qiqi Zhu
title	Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
title_short	Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
title_full	Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
title_fullStr	Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
title_full_unstemmed	Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images
title_sort	proportion estimation for urban mixed scenes based on nonnegative matrix factorization for high-spatial resolution remote sensing images
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/f222f83121c247d38e84f1b4eba40b4e
work_keys_str_mv	AT qiqizhu proportionestimationforurbanmixedscenesbasedonnonnegativematrixfactorizationforhighspatialresolutionremotesensingimages AT jialechen proportionestimationforurbanmixedscenesbasedonnonnegativematrixfactorizationforhighspatialresolutionremotesensingimages AT linlinwang proportionestimationforurbanmixedscenesbasedonnonnegativematrixfactorizationforhighspatialresolutionremotesensingimages AT qingfengguan proportionestimationforurbanmixedscenesbasedonnonnegativematrixfactorizationforhighspatialresolutionremotesensingimages
_version_	1718426084247076864

Proportion Estimation for Urban Mixed Scenes Based on Nonnegative Matrix Factorization for High-Spatial Resolution Remote Sensing Images

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