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Shape matching in (non-rigid) natural environments
Shape matching is an important principle in image processing to convert unordered 2D or 3D data into contiguous data structures. In rigid or man-made environments shape matching can be defined as correspondence problem between reference and unknown shapes. The matching error corre-sponds to the summed up distance between corresponding points along the matched shapes (Belongie, Malik 2002).
Finding tree crowns allows to model forest stands without precise knowledge about the true position of single tree stems. The pixel-wise difference in a canopy height model (CHM) does not yield robust estimates of tree positions and the often used curvature function (fourth-order polynomial within a 3x3 window) yields too many artifacts in urban areas and scattered forest stands. Therefore a specific shape matching approach for tree crowns has been developed, which tries to overcome the limited robustness of functional models. In a customized window, slope values from the center along 8 directions will be evaluated (yellow pattern in Figure 1). If the spatial evaluation identifies a tree crown candidate, the weighted spectral histogram verifies that hue, saturation and lightness are within feasible limits (yellow pattern in Figure 2).
Using a DSM with 1m ground resolution and color-infrared images (CIR ADS80) with 0.25cm allows to extract tree crown candidates (Figure 3). Height values below 3m are ignored (typically shrub) and values with low NDVI are reclassified as artificial objects like buildings (e.g. blue region in Figure 3).