Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments

Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, h...
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Gespeichert in:

Autor*in:	Yingbin Deng [verfasserIn] Changshan Wu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM)

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 8(2016), 4, p 349
Übergeordnetes Werk:	volume:8 ; year:2016 ; number:4, p 349

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs8040349

Katalog-ID:	DOAJ014533146

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allfieldsGer	10.3390/rs8040349 doi (DE-627)DOAJ014533146 (DE-599)DOAJ7081df2adf784aadba4b22fd1f1055eb DE-627 ger DE-627 rakwb eng Yingbin Deng verfasserin aut Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA. multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM) Science Q Changshan Wu verfasserin aut In Remote Sensing MDPI AG, 2009 8(2016), 4, p 349 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:8 year:2016 number:4, p 349 https://doi.org/10.3390/rs8040349 kostenfrei https://doaj.org/article/7081df2adf784aadba4b22fd1f1055eb kostenfrei http://www.mdpi.com/2072-4292/8/4/349 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 8 2016 4, p 349
allfieldsSound	10.3390/rs8040349 doi (DE-627)DOAJ014533146 (DE-599)DOAJ7081df2adf784aadba4b22fd1f1055eb DE-627 ger DE-627 rakwb eng Yingbin Deng verfasserin aut Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA. multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM) Science Q Changshan Wu verfasserin aut In Remote Sensing MDPI AG, 2009 8(2016), 4, p 349 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:8 year:2016 number:4, p 349 https://doi.org/10.3390/rs8040349 kostenfrei https://doaj.org/article/7081df2adf784aadba4b22fd1f1055eb kostenfrei http://www.mdpi.com/2072-4292/8/4/349 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 8 2016 4, p 349
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author	Yingbin Deng
spellingShingle	Yingbin Deng misc multiple endmember spectral mixture analysis (MESMA) misc class-based multiple endmember spectral mixture analysis (C-MESMA) misc support vector machine (SVM) misc Science misc Q Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments
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topic_title	Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM)
topic	misc multiple endmember spectral mixture analysis (MESMA) misc class-based multiple endmember spectral mixture analysis (C-MESMA) misc support vector machine (SVM) misc Science misc Q
topic_unstemmed	misc multiple endmember spectral mixture analysis (MESMA) misc class-based multiple endmember spectral mixture analysis (C-MESMA) misc support vector machine (SVM) misc Science misc Q
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title	Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments
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title_full	Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments
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title_sort	development of a class-based multiple endmember spectral mixture analysis (c-mesma) approach for analyzing urban environments
title_auth	Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments
abstract	Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA.
abstractGer	Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA.
abstract_unstemmed	Multiple endmember spectral mixture analysis (MESMA) has been widely applied for estimating fractional land covers from remote sensing imagery. MESMA has proven effective in addressing inter-class and intra-class endmember variability by allowing pixel-specific endmember combinations. This method, however, assumes that each land cover type has an equal probability of being included in the model, and the one with the least estimation error (e.g., root mean square error) was chosen as the “best-fit” model. Such an approach may mistakenly include a land cover class in the model and overestimate its abundance, or it might omit a class from the model and subsequently lead to underestimation. To address this problem, this paper developed a land cover class-based multiple endmember spectral mixture analysis (C-MESMA) method. In particular, a support vector machine (SVM) method with reflectance spectra and spectral indices, including the normalized difference vegetation index (NDVI), the biophysical composition index (BCI), and the ratio normalized difference soil index (RNDSI), were employed to classify the image into six land cover classes: pure impervious surface area (ISA), pure vegetation, pure soil, ISA-vegetation, vegetation-soil, and vegetation-ISA-soil. With the information of land cover classes, an individual MESMA method was applied to each mixed class. Finally, the fractional maps were derived through integrating land cover fractions of each land cover class. Quantitative analysis of the resulting percent ISA (%ISA) and comparative analyses with traditional MESMA indicate that C-MESMA improved the estimation accuracy of %ISA.
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title_short	Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments
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Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments

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