Reducing the impacts of intra-class spectral variability on soft classification and its implications for super-resolution mapping

Abstract

The impacts of intra-class spectral variation on the use of soft classification outputs for super-resolution mapping was assessed. The accuracy of soft classification and super- resolution mapping was negatively related to the degree of intra-class spectral variation present in the data set. The provision of a distribution of possible sub-pixel fractional covers from a soft classification may reflect the impacts of intra-class variation and help to enhance super-resolution mapping. A possible approach to reduce the impacts of intra-class spectral variation was investigated. This was based on an approach that reduces the degree of intra-class spectral variation by defining spectral subclasses for use in the soft classification. The use of this approach increased the accuracy of soft classification predictions from r = 0.87 to r = 0.94 and decreased the RMSE in super-resolution mapping of an inter-class boundary from 44.7 m to 37.2 m. The results highlighted that reducing intra-class spectral variation may be used to increase the accuracy of soft classification and super-resolution mapping.