Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zhenfeng Shao [verfasserIn] Yuan Zhang [verfasserIn] Cheng Zhang [verfasserIn] Xiao Huang [verfasserIn] Tao Cheng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Impervious surface Spectral Mixture Analysis(SMA) hierarchical strategy endmember class spatially varied endmember spectra

Übergeordnetes Werk:	In: Geo-spatial Information Science - Taylor & Francis Group, 2017, 25(2022), 4, Seite 550-567
Übergeordnetes Werk:	volume:25 ; year:2022 ; number:4 ; pages:550-567

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1080/10095020.2022.2028535

Katalog-ID:	DOAJ008870918

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520			\|a ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.
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callnumber-first	G - Geography, Anthropology, Recreation
author	Zhenfeng Shao
spellingShingle	Zhenfeng Shao misc GA1-1776 misc QB275-343 misc Impervious surface misc Spectral Mixture Analysis(SMA) misc hierarchical strategy misc endmember class misc spatially varied endmember spectra misc Mathematical geography. Cartography misc Geodesy Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution
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topic_title	GA1-1776 QB275-343 Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution Impervious surface Spectral Mixture Analysis(SMA) hierarchical strategy endmember class spatially varied endmember spectra
topic	misc GA1-1776 misc QB275-343 misc Impervious surface misc Spectral Mixture Analysis(SMA) misc hierarchical strategy misc endmember class misc spatially varied endmember spectra misc Mathematical geography. Cartography misc Geodesy
topic_unstemmed	misc GA1-1776 misc QB275-343 misc Impervious surface misc Spectral Mixture Analysis(SMA) misc hierarchical strategy misc endmember class misc spatially varied endmember spectra misc Mathematical geography. Cartography misc Geodesy
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title	Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution
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title_sort	mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution
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title_auth	Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution
abstract	ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.
abstractGer	ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.
abstract_unstemmed	ABSTRACTImpervious surface mapping is essential for urban environmental studies. Spectral Mixture Analysis (SMA) and its extensions are widely employed in impervious surface estimation from medium-resolution images. For SMA, inappropriate endmember combinations and inadequate endmember classes have been recognized as the primary reasons for estimation errors. Meanwhile, the spectral-only SMA, without considering urban spatial distribution, fails to consider spectral variability in an adequate manner. The lack of endmember class diversity and their spatial variations lead to over/underestimation. To mitigate these issues, this study integrates a hierarchical strategy and spatially varied endmember spectra to map impervious surface abundance, taking Wuhan and Wuzhou as two study areas. Specifically, the piecewise convex multiple-model endmember detection algorithm is applied to automatically hierarchize images into three regions, and distinct endmember combinations are independently developed in each region. Then, spatially varied endmember spectra are synthesized through neighboring spectra using the distance-based weight. Comparative analysis indicates that the proposed method achieves better performance than Hierarchical SMA and Fixed Four-endmembers SMA in terms of MAE, SE, and RMSE. Further analysis suggests that the hierarchical strategy can expand endmember class types and considerably improve the performance for the study areas in general, specifically in less developed areas. Moreover, we find that spatially varied endmember spectra facilitate the reduction of heterogeneous surface material variations and achieve the improved performance in developed areas.
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title_short	Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution
url	https://doi.org/10.1080/10095020.2022.2028535 https://doaj.org/article/a49a4905d24b4f64bbce144cf73c09eb https://www.tandfonline.com/doi/10.1080/10095020.2022.2028535 https://doaj.org/toc/1009-5020 https://doaj.org/toc/1993-5153
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Mapping impervious surfaces with a hierarchical spectral mixture analysis incorporating endmember spatial distribution

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