Identifying the Characteristic Scale of Scene Variation in Fine Spatial Resolution Imagery With Wavelet Transform-based Sub-image Statistics

Understanding the spatial structure of fine spatial resolution images is instrumental for either pixel-or object-based image analysis.In this paper,the characteristic scale of scene variation in images is evaluated using statistics of sub-images produced by a wavelet transform And the semivariances of the images is calculated.As an example,the study analyze the city landscape,farmland landscape,forest landscape and road and green belt landscape pattern on the Quickbird image(spatial resolution 2.4m).It was found that with energy signature images,the change rate of SD over spatial resolution range between two successive decomposition levels(ΔSD/ΔR) suggested a synoptic and approximate description for the characteristic scale of scene variance.It is feasible that to evaluate the scene variation using wavelet-based methodology.The semivariance analysis can detect the landscape structure overall average pattern on the experimental data,but it is not as sensitive as wavelet-base methodology to directional structure characteristics scale analysis.The semivariance would do well when the more experience or knowledge about the study area was input.