Clustering green openspace using UAV (Unmanned Aerial Vehicle) with CNN (Convolutional Neural Network)

The latest in unmanned aerial vehicles (UAVs) and associated sensing systems make these increasingly attractive platforms to the remote sensing community. A large number of spatial details contained in these images opens the door for advanced monitoring applications. In this paper, we use this cost-effective and attractive technology for the automatic detection of green open spaces. Given a UAV image of trees acquired, then, we analyze these Convolutional Neural Networks (CNN) points of the prior classifier trained on a set of trees and no trees points. As output, CNN will mark each detected tree by super pixel. Then, in order to capture the shape of each tree, we propose to merge this pixel-level segmentation with a method based active contour on the Color threshold. Finally, we further analyze the texture of regions with pixel-level segmentation and use summing pixel to distinguish trees from other vegetation. Experimental results obtained in UAV images from extensive calculations using the program that has been made and the existing provisions get a result of error of 7.256% on the first trial, the second experiment is 5.156%, and the third experiment is 3.126%.