Youngentob, Kara Nicole; Roberts, Dar A.; Held, Alex; Dennison, Philip E.; Jia, Xiuping; Lindenmayer, David
Successful discrimination of a variety of natural and urban landscape components has been achieved with remote sensing data using multiple endmember spectral mixture analysis (MESMA). MESMA is a spectral matching algorithm that addresses spectral variability by allowing multiple reference spectra (i.e., endmembers) to represent each material class. However, materials that have a high-degree of spectral similarity between classes, such as similar plant-types or closely related plant species, and...[Show more]
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