Individual Tree Detection from High Spatial Resolution Imagery Using Color and Texture Features

Abstract

An automatic individual tree detection method from pure image is proposed. Color and texture features are selected to form a vector for a pixel-level classification, then trained to assign a label to each pixel. Other features can be integrated into the pixel vector for extracting more information of trees. An ensemble method combining multiple logistic regression classifiers improves effectiveness of the single pixel-level classifier. Then spectral, shape and knowledge characteristics of individual tree crowns are used for tree top localization. At last, tree crowns are delineated by region-based algorithm.