The Positional Effect in Soft Classification Accuracy Assessment

Abstract

Recent research has included the rapid development of soft classification algorithms and soft classification accuracy assessment beyond the traditional hard approaches. However, less consideration has been given to whether conditions and assumptions generated for the hard classification accuracy assessment are appropriate for the soft one. Positional error is one of the most significant uncertainties that need to be considered. This research examined the impacts of positional errors on the accuracy measures derived from the soft error matrix using NLCD 2011 as reference data and several coarser maps generated from NLCD 2011 as classification maps at the spatial resolutions of 150m, 300m, 600m, and 900m. Eight study sites, with a spatial extent of 180km×180km, of different landscape characteristics were investigated using a two-level classification scheme. Results showed that with existing registration accuracies achieved by current global land cover mapping, the errors in overall accuracy (OA-error) were 2.13% -39.98% and 2.53%-48.82% for the 8 and 15 classes, respectively and the errors in Kappa (Kappa-error) were 6.64%-57.09% and 7.08%-58.81% for the 8 and 15 classes, respectively if soft classifications were implemented based on images where spatial resolutions varied from 150m to 900m. More complex landscape characteristics and classes in the classification scheme produced a greater impact of the positional error on the accuracy measures. To keep both OA-error and Kappa-error under 10 percent, the average required registration accuracy should achieve 0.1 pixels. This paper strongly recommends the addition of uncertainty analysis due to positional error in future global land cover mapping.