M-CORE: A novel approach for land cover fraction mapping using multisite spectral libraries

The concept of a generic spectral library (GSL), i.e., a structured collection of image spectra sampled from various optical remote sensing images covering different sites and points in time, has been suggested in previous studies to
facilitate the tedious process of producing training data for remote sensing-based land cover (LC) mapping.When using multisource libraries collected over different sites, library pruning approaches are needed to extract an apt set of labeled spectra to perform mapping on a specific area. Library pruning mainly
aims to discard image-irrelevant and redundant spectra, while limiting spectral confusion during the mapping process. Most library pruning approaches focus only on one of these aspects, which has been shown to negatively affect mapping accuracies.
This article emphasizes the need for a multistep approach to
optimize a GSL for site-specific mapping. We propose a new
library pruning method called M-CORE, specifically designed
to facilitate LC fraction mapping. The method extends multiple
signal classification (MUSIC) with a confusion reduction (CORE)
component. Vegetation-impervious-soil (VIS) fraction mapping
experiments on a Sentinel-2 image of Brussels, using a GSL with
and without local spectra, show the added value of M-CORE over
previously proposed library pruning methods and demonstrate
the feasibility of GSL-based mapping in an urban context.