Individual Deciduous Tree Recognition in Leaf-Off Aerial Ultrahigh Spatial Resolution Remotely Sensed Imagery

This study proposed and tested a multistep method for the recognition of individual deciduous trees in leaf-off aerial ultrahigh spatial resolution remotely sensed (UHSRRS) imagery. This topic has received limited coverage in previous endeavors, which focused mainly on the detection and delineation of coniferous trees in remotely sensed images with relatively lower spatial resolutions. Thus, the traditional algorithms tend to fail in case of the referred scenario. In order to fill this technical gap, an algorithm that joins mathematical morphological operations and marker-controlled watershed segmentation was first assumed for the extraction of single trees in UHSRRS images. Next, a distribution-free support vector machine (SVM) classifier was applied to distinguish the extracted segments as deciduous or coniferous trees, merely in terms of two newly-derived morphological features. Experimental evaluations indicated that the integral solution plan can extract and classify the deciduous and coniferous trees in the leaf-off aerial UHSRRS images of local dense forest for test with correctness over 92% and 70%, respectively. Overall, the recognition results with > 66% correctness have primarily validated the proposed technique.