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Aerial photographs sometimes suffer from artifacts caused by vignetting effects and changing topographic sun-canopy-sensor geometry. In this paper, we present an empirical image restoration method that is based on multiscale relationships of image structures. The fine-scale image structures depict tree crowns in a deciduous forest and serve as units in the restoration process. The color image is initially converted to the intensity, hue, saturation (IHS) system. For the I-band, two different types of variables are estimated for each segment: the local intensity difference of neighboring segments (affinity) and the mean intensity per segment. For the H- and S-bands, the mean value per segment is estimated. Regression analysis is used to model the relationship of these four variables with the coarse-scale intensity values of the corresponding segments. The correction results in new feature values that are uncorrelated with the coarse-scale intensity values. The method is evaluated on three digital aerial photographs with a ground reference dataset from the Eastern Deciduous Forest in West Virginia, USA. The image correction method is shown to result in a significant improvement for tree species classification. Index Terms--Fuzzy affinity, Gaussian smoothing, image restoration, scale-space theory, vignette correction.

Leaf-off individual trees in a deciduous forest in the eastern USA are detected and analysed in small footprint, high sampling density lidar data. The data were acquired February 1, 2001, using a SAAB TopEye laser profiling system, with a sampling density of approximately 12 returns per square meter. The sparse and complex configuration of the branches of the leaf-off forest provides sufficient returns to allow the detection of the trees as individual objects and to analyse their vertical structures. Initially, for the detection of the individual trees only, the lidar data are first inserted in a 2D digital image, with the height as the pixel value or brightness level. The empty pixels are interpolated, and height outliers are removed. Gaussian smoothing at different scales is performed to create a three-dimensional scale-space structure. Blob signatures based on second-order image derivatives are calculated, and then normalised so they can be compared at different scale-levels. The grey-level blobs with the strongest normalised signatures are selected within the scale-space structure. The support regions of the blobs are marked one-at-a-time in the segmentation result image with higher priority for stronger blobs. The segmentation results of six individual hectare plots are assessed by a computerised, objective method that makes use of a ground reference data set of the individual tree crowns. For analysis of individual trees, a subset of the original laser returns is selected within each tree crown region of the canopy reference map. Indices based on moments of the first four orders, maximum value and number of canopy and ground returns, are estimated. The indices are derived separately for height and laser reflectance of branches for the two echoes. Significant differences (p<0.05) are detected for numerous indices for three major native species groups: oaks (Quercus spp.), red maple (Acer rubrum) and yellow poplar (Liriodendron tuliperifera). Tree species classification results of different indices suggest a moderate to high degree of accuracy using single or multiple variables. Furthermore, the maximum tree height is compared to ground reference tree height for 48 sample trees and a 1.1-m standard error (R2=68% (adj.)) within the test-site is observed. [Copyright &y& Elsevier]