TY - GEN
T1 - Clustering green openspace using UAV (Unmanned Aerial Vehicle) with CNN (Convolutional Neural Network)
AU - Fikri, Moh Yanni
AU - Azzarkhiyah, Khafid
AU - Al Firdaus, Muhammad Juan
AU - Winarto, Tommy Andreas
AU - Syai'In, Mat
AU - Adhitya, Ryan Yudha
AU - Endrasmono, Joko
AU - Rahmat, Mohammad Basuki
AU - Setiyoko, Annas Singgih
AU - Fathulloh,
AU - Zuliari, Efrita Arfah
AU - Budianto, Agus
AU - Soeprijanto, Adi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - 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%.
AB - 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%.
KW - Convolutional Neural Network
KW - Unmanned Aerial Vehicles
KW - super pixel
UR - http://www.scopus.com/inward/record.url?scp=85076346997&partnerID=8YFLogxK
U2 - 10.1109/ISESD.2019.8909502
DO - 10.1109/ISESD.2019.8909502
M3 - Conference contribution
AN - SCOPUS:85076346997
T3 - Proceeding - 2019 International Symposium on Electronics and Smart Devices, ISESD 2019
BT - Proceeding - 2019 International Symposium on Electronics and Smart Devices, ISESD 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Symposium on Electronics and Smart Devices, ISESD 2019
Y2 - 8 October 2019 through 9 October 2019
ER -