The impact of image and class structure upon sub-pixel mapping accuracy using the pixel-swapping algorithm

Abstract

A series of tests assess the impact of image and class structure within input imagery upon the accuracy of outputs from the pixel-swapping algorithm (PSA) to better assess the viability of this sub-pixel mapping algorithm to spatially locate class proportions and improve soft classification accuracies. Simulated maps with known spatial autocorrelation, class proportion and mixed pixel percentages are sub-pixel mapped at varying zoom factors using the PSA and accuracies measured. Block majority filtering (BMF) of input images is an experimental accuracy control. Results quantify PSA accuracies compared to the BMF based on the interrelationship between spatial autocorrelation, class proportions, zoom factor and mixed pixel quantities. Findings, while validating the known positive correlation between spatial autocorrelation and PSA accuracy, demonstrate class proportion difference and mixed pixel quantities to be positively and negatively related to PSA accuracy, respectively. Class proportion affects PSA accuracy to a greater degree on maps exhibiting low spatial autocorrelation levels versus maps with high spatial autocorrelation levels. Traditional accuracy measures, such as overall accuracy, are shown to overestimate the true performance of the PSA. The difference in overall accuracy of the PSA between all map pixels and within mixed map pixels alone can be over 20%. Overall, in general, the PSA accurately sub-pixel mapped input images with an approximate minimum level of spatial autocorrelation at a Moran's I of 0.63, a maximum of 30% of mixed pixels relative to entire image pixels, a difference in class proportions greater than 40% and at zoom factors below 10.