An evaluation of Multiple Endmember Spectral Mixture.
The spectral mixture model analyzes individual linear spectral mixtures for each permutation of two or more endmembers of the spectral library, in which no more than one endmember from a surface cover class is present (i.e., at most one snow endmember).
A spectral mixing technique called Multiple Endmember Spectral Mixture Analysis (MESMA) is at the core of VIPER Tools. MESMA is an extension of simple Spectral Mixture Analysis (SMA). In simple SMA, a spectrum is modelled as the sum of pure spectra called endmembers, each weighted by the fraction of an endmember required to produce the mixture (; (Roberts1993); ).
Multiple Endmember Spectral Mixture Analysis (MESMA): SMA is a commonly used method for subpixel detection and classification of remotely sensed data(1-6).
Grace Njenga March, 2016 SUPERVISORS: Ms. Dr. Ir. W. Bijker Dr. V.A. Tolpekin Multiple Endmember Spectral Mixture Analysis (MESMA) on multi-temporal VHR images for weed detection in.
Multiple endmember spectral mixture analysis (MESMA) addresses these problems by testing multiple combinations of endmembers and endmember spectra for each pixel in the image (Roberts et al., 1998). Thus, MESMA increases the flexibility of simple SMA.
Spectral mixture analysis (SMA), a scheme of sub-pixel-based classifications, is one of the widely used models to map fractional land use and land cover information in remote sensing imagery. It assumes that: 1) a mixed pixel is composed by several pure land cover classes (endmembers) linearly or nonlinearly, and 2) the spectral signature of each endmember is a constant within the entire.
Multiple endmember spectral mixture analysis (MESMA) models mixed spectra as a linear combination of endmembers that are allowed to vary in number and type on a per pixel basis. For modeling an image using MESMA, a parsimonious set of endmembers is desirable for computational efficiency and operational simplicity. This.