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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| Format: | Article |
| Language: | English |
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MDPI AG
2016-04-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/8/4/349 |
| _version_ | 1819290655791251456 |
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| author | Yingbin Deng Changshan Wu |
| author_facet | Yingbin Deng Changshan Wu |
| author_sort | Yingbin Deng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T03:26:12Z |
| format | Article |
| id | doaj.art-7081df2adf784aadba4b22fd1f1055eb |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:26:12Z |
| publishDate | 2016-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7081df2adf784aadba4b22fd1f1055eb2022-12-21T17:17:19ZengMDPI AGRemote Sensing2072-42922016-04-018434910.3390/rs8040349rs8040349Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban EnvironmentsYingbin Deng0Changshan Wu1Department of Geography, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USADepartment of Geography, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, USAMultiple 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.http://www.mdpi.com/2072-4292/8/4/349multiple endmember spectral mixture analysis (MESMA)class-based multiple endmember spectral mixture analysis (C-MESMA)support vector machine (SVM) |
| spellingShingle | Yingbin Deng Changshan Wu Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments Remote Sensing multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM) |
| title | Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments |
| title_full | Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments |
| title_fullStr | Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments |
| title_full_unstemmed | Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments |
| title_short | Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments |
| title_sort | development of a class based multiple endmember spectral mixture analysis c mesma approach for analyzing urban environments |
| topic | multiple endmember spectral mixture analysis (MESMA) class-based multiple endmember spectral mixture analysis (C-MESMA) support vector machine (SVM) |
| url | http://www.mdpi.com/2072-4292/8/4/349 |
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