* Classification maps generated by mixture modeling*

This slide illustrates the application of a new algorithm for solving the mixture modeling problem to a remotely sensed image of part of Africa. Mixture modeling allows environmental scientists to estimate the proportions of different vegetation types present in a single pixel, thereby characterizing the vegetation more realistically than a classification result that labels each pixel as a single vegetation type. Accurate descriptions of the land surface are important boundary conditions for climate models and other types of global environmental models. The mixture modeling problem involves estimating the proportions of different vegetation types from remotely sensed images. These proportions are estimated by comparing the observed reflectance measurements within a pixel to the expected measurements one would obtain if the pixel were purely of one ground type, and solving for the proportions using mathematical optimization procedures. The classical methods employed by environmental scientists to solve this problem suffer from a variety of numerical instabilities and computational deficiencies; while the new algorithm developed at Maryland - based on solutions to similar problems in image restoration - provide results that are more accurate using algorithms that are faster.

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