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Jon Morton
Observed name:
J.A. Morton
Institution:
Meta (United States)
Observed institutions:
United States Army Corps of Engineers,
Aston University,
Meta (United States)
OpenAlex ID:
A5083996385
Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system
2018
·
Tao Liu
, Amr Abd-Elrahman
, et al.
·
GIScience & Remote Sensing
·
273
PDF
Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery
2017
·
Roshan Pande-Chhetri
, Amr Abd-Elrahman
, et al.
·
European Journal of Remote Sensing
·
151
PDF
Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers
2018
·
Tao Liu
, Amr Abd-Elrahman
, et al.
·
GIScience & Remote Sensing
·
22
PDF
Feature-Align Network with Knowledge Distillation for Efficient Denoising
2022
·
Lucas D. Young
, Fitsum A. Reda
, et al.
·
IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
·
17
PDF
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars
2023
·
Xiaoyu Xiang
, Jon Morton
, et al.
·
IEEE Winter Conference on Applications of Computer Vision
·
3
PDF
Feature-Align Network and Knowledge Distillation for Efficient Denoising.
2021
·
Lucas D. Young
, Fitsum A. Reda
, et al.
·
arXiv (Cornell University)
The Use Of Coregistered Landsat MSS, TM And SIR-A Imagery for Lithological Mapping
2005
·
R.B. Oldfield
, J. Elgy
, et al.
·
IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Works count:
7
Citation count:
466
H-index:
4
I10-index:
4
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