Spatial analysis of remote sensing image classification accuracy.

The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty. This research uses geographically weighted approaches to model the spatial variations in the accuracy of both (crisp) Boolean and (soft) fuzzy land cover classes. Remotely sensed data were classified using a maximum likelihood classifier and a fuzzy classifier to predict Boolean and fuzzy land cover classes respectively. Field data were collected at sub-pixel locations and used to generate soft and crisp validation data. A Geographically Weighted Regression was used to analyse spatial variations in the relationships between observations of Boolean land cover in the field and land cover classified from remote sensing imagery. A geographically weighted difference measure was used to analyse spatial variations in fuzzy land cover accuracy. Maps of the spatial distribution of accuracy were created for fuzzy and Boolean classes. This research demonstrates that data collected as part of a standard remote sensing validation exercise can be used to estimate mapped, spatial distributions of accuracy that would augment standard accuracy measures reported in the error matrix. It suggests that geographically weighted approaches, and the spatially explicit representations of accuracy they support, offer the opportunity to report land cover accuracy in a more informative way.