Spatially Constrained Multiple Endmember Spectral Mixture Analysis for Quantifying Subpixel Urban Impervious Surfaces

Multiple endmember spectral mixture analysis (MESMA) has been extensively employed to accommodate endmember variability associated with the mixed pixel problem in remote sensing imagery. However, endmember extraction is a critical step in the application of MESMA. Considering that spatial information can be helpful for selecting local representative endmembers, this paper develops a spatially constrained MESMA method, with which multiple endmembers for each class are automatically derived within a predefined neighborhood. Two specific novelties are: 1) to identify all the endmembers over the whole image scene for each class through a classification tree approach; and 2) to generate spatially constrained endmembers for the neighborhood of each target pixel of the image through a k-means clustering method. MESMA is then performed using the derived spatially constrained endmembers. This proposed method was applied to a Landsat Enhanced Thematic Mapper (ETM+) image for examining subpixel urban impervious surfaces, and its performance was compared with that of a global MESMA method. The results suggest that spatially constrained MESMA is able to yield adequate estimates, supported by a relatively decent precision and low bias (10.68% for mean absolute error and -3.58% for systematic error).