Image restoration based on multiscale relationships of image structures

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 Hand 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.