Tree crown recognition using high spatial resolution remotely sensed imagery provides useful information relating the number and distribution of trees in a landscape. A common technique used to identify tree locations uses a local maximum (LM) filter with a static-sized moving window. LM techniques operate on the assumption that high local radiance values represent the centroid of a tree crown. While success has been found using LM techniques various authors have noted the introduction of error through the inclusion of falsely identified trees. Missing trees, or omission error, are primarily the result of too coarse an image spatial resolution (in relation to the size of the trees present). Falsely indicated trees (commission error) may be removed through image processing post-LM filtering. In this paper we present a variety of techniques for addressing commission error when applying a LM technique. Methods exploiting spatial and spectral information are applied. The best results, where the number of correct trees is high with few false positives, are found for a spatial filter applied to LM generated within variable window sized as suggested by image spatial structure.
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