The effects of spatial resolution on the classification of Thematic Mapper data

Abstract Actual and degraded LANDSAT-4 Thematic Mapper (TM) data were analysed to examine the effect of spatial resolution on the performance of a per pixel, maximum-likelihood classification algorithm. Analysis of variance (ANOVA) and a balanced, three-factor, eight-treatment, fixed-effects model were used to investigate the interactions between spatial resolution and two other TM refinements, spectral band configuration and data quantization. The goal was to evaluate quantitatively the effects of these attributes on classification accuracies obtained with all pixels (pure pixels plus mixed pixels) and on accuracies obtained with pure pixels alone. A comparison of results from these separate analyses supported previous explanations of the effects of increasing spatial resolution. First, the difficulty in classifying mixed pixels was demonstrated by an average 21 per cent decrease in percentage accuracy from the pure-pixel case to the pure-plus-mixed-pixel case for the eight ANOVA treatments. In the pure-pixel case, an increase in spatial resolution from 80 to 30 m caused an average 6·1 per cent decrease in percentage accuracy when the other factors were held constant. This decrease was attributed to increased within-class spectral variability at the TM resolution. Finally, in the pure-plus-mixed-pixel case, increasing the spatial resolution did not significantly affect accuracy. This insignificance was attributed to a reduced proportion of mixed pixels at the TM resolution which counteracted the detrimental effects of increased spectral variability. These results point to a need for the development of new approaches to classification which take full advantage of the TM spatial resolution.