An Object-Specific Image-Texture Analysis of H-Resolution Forest Imagery

A n e w structural image-texture technique, termed the triangulated primitive neighborhood method (TPN), is employed to investigate the variable spatial characteristics of high-resolution forest objects, as modeled by a Compact Airborne Spectrographic Imager data set. Based on current psychophysical texture theory, this technique incorporates location-specific primitives and a variablesized and shaped moving kernel to automatically provide objectand area-specific regularized images. These objectrich, but variance-reduced images allow a traditional classifier to be used on a complex high-resolution forest data set with improved accuracy. The robustness of this technique is evaluated by comparing the maximum likelihood classification accuracy of nine forest classes generated from a combination of the grey level cooccurrence matrix method, semivariance, and customized filters, against those derived from the TPN method. By including into the classification scheme an object-specific channel that models crown density, the highest overall classification accuracy (78 %)from all techniques is achieved with the TPN method.