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

Yingbin Deng,Changshan Wu

doi:10.3390/rs8040349

Yingbin Deng, Changshan Wu

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Journal: Remote Sensing	Publication Date: Apr 21, 2016
Citations: 21	License type: CC BY 4.0

Affiliation: University of Wisconsin–Milwaukee

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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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Spectral mixture analysis (SMA) has been widely applied to address the mixed pixel problem, a typical issue associated with medium- and coarse-resolution remote sensing imagery [1,2,3,4]
Vegetation-impervious surface area (ISA) was the major land cover type in the residential area, which was located outside the central business district (CBD) region
A novel approach called land cover-class based multiple endmember spectral mixture analysis (C-MESMA), which combines the pixel-based supervised classification and MESMA, is proposed to extract the fractions of the ISA, vegetation, and soil

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Spectral mixture analysis (SMA) has been widely applied to address the mixed pixel problem, a typical issue associated with medium- and coarse-resolution remote sensing imagery [1,2,3,4]. SMA assumes that each image pixel is comprised of several land cover classes, each of which has distinctive spectral signatures [1,5]. The capability of traditional SMA models in dealing with complex urban and suburban landscapes has been questioned, as the few endmembers may not be able to represent their corresponding land cover classes [11,12,13]. As an improved version of SMA, multiple endmember spectral mixture analysis (MESMA)

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