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- W2158598977 abstract "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 can 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." @default.
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- W2158598977 date "2007-08-01" @default.
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- W2158598977 title "Variability in Soft Classification Prediction and its implications for Sub-pixel Scale Change Detection and Super Resolution Mapping" @default.
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- W2158598977 doi "https://doi.org/10.14358/pers.73.8.923" @default.
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