On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation

Integration of spectral and multi‐scale texture is proposed in order to improve the detection and classification of urban‐trees from QuickBird imagery. Arguing that spatial‐structure semantic information exits at a hierarchy of scales and that texture is a consequence of objects in the hierarchy, multi‐scale wavelets decomposition is proposed for the extraction of vertical, horizontal and diagonal texture components. Pre‐selection of texture sub‐bands is achieved via mean, entropy, variance and second angular moment. The resulting sub‐bands are analysed for separability between trees and similarly reflecting features, such as rice‐paddy, grass/lawns, open ground and playground, based on Kullback–Leibler (KL) divergence and Battacharyya distance. The results are ranked and classified with k‐means. In comparison with the field data, KL gave the best results with omission and commission error of 4.4%. The proposed methodology has the ability to capture the increased natural variability in reflectance and improved the accuracy by 23.6%, in comparison with spectral‐only.