A Geographically Weighted Total Composite Error Analysis for Soft Classification

Abstract

Errors in land cover classification are often spatially heterogeneous even though a soft classification model such as spectral unmixing is implemented to mitigate a mixed pixel problem. The estimated land covers are fractions of targeted classes with the restriction of the sum to one and being non-negative. To assess the classification with considering a spatial heterogeneity, we propose a geographically weighted total composite error analysis. By using the USGS global reference database, we assessed errors of spectral unmixing classification of ALOS AVNIR-2 data into 4 land cover classes. Results yield a spatial surface of local errors by the Aitchison distance and address that the error magnitude across space is associated with the complexity of land covers.