Spatial context-dependent multi-scale and directional image texture

This study evaluated image texture within a multi-scale context. The multi-scale texture profile is a representation of texture as a function of scale. It is related to the variogram, but has the advantage of being a direct measure of local texture over a specified kernel. Directional texture is a method to capture the dependence of texture on orientation using 1-dimensional kernels, and is potentially useful for quantifying texture for classes with a linear shape, such as roads or rivers. Seven derived texture attributes were proposed from the multi-scale texture profile and directional texture: the minimum, median, mean, maximum, range, minimum directional, and minimum multi-scale and directional texture. The derived texture attributes were found to be useful in developing a locally adaptive texture measure, which uses different texture attributes for different locations in the image. In order to develop the rules for the adaptive method, seven characteristic multi-scale texture profiles were identified. Rules were established to identify the general classes, and each pixel was assigned to one class only, depending on that pixel's texture profile. Optimal texture measures were proposed for each class. In an analysis using selected texture classes from IKONOS test data from Jeju, Korea, it was found that compared to all other texture measures, the adaptive texture provided the highest average and minimum classification accuracy, as well as the second highest maximum accuracy (69%, 51% and 92%, respectively). In comparison, the best fixed size kernel, 11 × 11 pixels, had accuracies in the same categories of 68%, 38% and 87%, and the original panchromatic image band had accuracies of 41%, 5% and 92%. Future work is needed to enhance the texture rules, and apply the adaptive texture method to other scales of data.