Variability in soft classification prediction and its implications for sub-pixel scale change detection and super resolution mapping

The impact of intra-class spectral variability on the estimation of sub-pixel land-cover class composition with a linear mixture model is explored. It is shown that the nature of intra-class variation present has a marked impact on the accuracy of sub-pixel class composition estimation, as it violates the assumption that a class can be represented by a single spectral endmember. It is suggested that a distribution of possible class compositions con be derived from pixels instead of a single class composition prediction. This distribution provides a richer indication of possible subpixel class compositions and highlights a limitation for super-resolution mapping. Moreover, the class composition distribution information may be used to derive different scenarios of changes when used in a post-classification comparison type approach to change detection. This latter issue is illustrated with an example of forest cover change in Brazil from Landsat TM data.