Abstract:
Automatic detection and classification of trees by using remotely analyzed information have been a dream of the many scientists, and land use administrators. The motivation for this problem comes from pollen tree excavation issue, automated 3D town modeling, urban planning and forestation, within which such information is employed to come up with the models.
Here, we offer an automatic methodology for individual tree detection and classification through aerial imagery using unmanned aerial vehicles (UAV), which is a rapidly evolving, cost effective and economical technology.
Firstly, the model is trained for the purpose of tree detection per image pixel by assigning a {tree, non-tree} label to each pixel in an aerial image. Afterwards, the output is refined into clean segmented image based upon which, we implement pattern matching to locate the separable tree crowns, which are then classified on the basis of tree species type with our algorithm.
We have verified the algorithm on many gathered aerial pictures across varied zones of a district and have confirmed excellent quality results with a good scalability of our proposed methodology. In contrast, most of formerly done work used costly hardware like multispectral images for tree detection and classification. Thus, our proposed technique has the potential to classify individual trees in an exceedingly cost-effective manner. This will be a usable tool for several forest researchers, managements, and also for the concerned government bodies to detect and excavate pollen trees, to fight with this seasonal pollen allergy war.
