TY - GEN
T1 - Deep Learning Techniques on Very High Resolution Images for Detecting Trees and Their Health Conditions
AU - Al-Mulla, Yaseen
AU - Ali, Ahsan
AU - Parimi, Krishna
N1 - DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2023/7/16
Y1 - 2023/7/16
N2 - Very high-resolution remote sensing imagery and imagery from unmanned aerial vehicles have been acknowledged as well as valued in recent years for a variety of purposes, especially in object detection. On the other hand, deep learning (DL) has evolved as a tool for assessing pattern recognition applications and standard machine learning techniques. This study applied several DL applications in the Sultanate of Oman for detecting trees and examining their health status using very high-resolution satellite imagery data. The DL model efficiently distinguished the date palm trees from other plants and other land uses, according to our results. Aside from date palms, the model developed in this study can serve as a starting point for models to identify other types of diseased plants and trees.
AB - Very high-resolution remote sensing imagery and imagery from unmanned aerial vehicles have been acknowledged as well as valued in recent years for a variety of purposes, especially in object detection. On the other hand, deep learning (DL) has evolved as a tool for assessing pattern recognition applications and standard machine learning techniques. This study applied several DL applications in the Sultanate of Oman for detecting trees and examining their health status using very high-resolution satellite imagery data. The DL model efficiently distinguished the date palm trees from other plants and other land uses, according to our results. Aside from date palms, the model developed in this study can serve as a starting point for models to identify other types of diseased plants and trees.
KW - AVHR
KW - Deep learning
KW - Drone
KW - Remote Sensing
UR - http://www.scopus.com/inward/record.url?scp=85178320836&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85178320836&partnerID=8YFLogxK
U2 - 10.1109/IGARSS52108.2023.10282430
DO - 10.1109/IGARSS52108.2023.10282430
M3 - Conference contribution
AN - SCOPUS:85178320836
SN - 979-8-3503-3174-5
T3 - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
SP - 6542
EP - 6544
BT - IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
CY - Pasadena, CA, USA
T2 - 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023
Y2 - 16 July 2023 through 21 July 2023
ER -
