Correlation of Object-based Texture Measures at Multiple Scales in Sub-decimeter Resolution Aerial Photography

Texture measures are commonly used to increase the number of input bands in order to improve classification accuracy, especially for panchromatic or true colour imagery. While the use of texture measures in pixel-based analysis has been well documented, this is not the case for texture measures calculated in an object-based environment. Because texture calculations are computer intensive, fewer variables are preferred and knowledge of correlation and how it changes across segmentation scales is required. The objectives of this study were to assess correlations between texture measures as a function of segmentation scale while mapping rangeland vegetation structure groups using 5-cm resolution true-color aerial photography. Entropy, mean and correlation were least correlated with other texture measures at all scales. The highest correlation that remained stable across all segmentation scales was found for contrast and dissimilarity. We observed both increasing and decreasing correlation coefficients for texture pairs as segmentation scale increased, and there was larger variability from one scale to the next at finer segmentations and more consistency in correlation at medium to coarse scales. This was attributed to the fact that at finer segmentation scales, smaller objects were more numerous, and the ratio of edge to interior pixels for an image object was higher than at coarser scales. This approach allowed for determining the most suitable and uncorrelated texture measures at the optimal image analysis scale, was less computer intensive than a series of test classifications, and shows promise for incorporation into rangeland monitoring protocols with very high resolution imagery.