TU4.R14.5

A MACHINE LEARNING APPROACH FOR HIGH RESOLUTION FRACTIONAL VEGETATION COVER ESTIMATION USING PLANET CUBESAT AND RGB DRONE DATA FUSION

Jacob Nesslage, Brittany Lopez Barreto, Adam Weingram, Erin Hestir, University of California, Merced, United States
Session:
TU4.R14: Machine Learning for Image Processing and Synthesis Oral
Track:
AI and Big Data
Location:
Room E
Presentation Time:
Tue, 18 Jul, 16:33 - 16:45 Pacific Time (UTC -7)
Session Chair:
Ilke Demir, Intel Labs
Session Manager:
Ayoti Banerjee
Presentation
Not logged in.
Discussion
Not logged in.
Resources
No resources available.
Session TU4.R14
TU4.R14.1: A NEW APPROACH FOR DOWNSCALING SOIL MOISTURE BY MERGING CONDITIONAL ADVERSARIAL NETWORKS AND PHYSICS-BASED PASSIVE MICROWAVE RETRIEVAL
Archana Kannan, University of Southern California, United States; Grigorios Tsagkatakis, Foundation for Research and Technology-Hellas (FORTH), Greece; Mahta Moghaddam, University of Southern California, United States
TU4.R14.2: DEEPFAKE SATELLITE IMAGERY DETECTION WITH MULTI-ATTENTION AND SUPER RESOLUTION
Umur Aybars Ciftci, Binghamton University, United States; Ilke Demir, Intel Labs, United States
TU4.R14.3: EFFICIENT AND RELIABLE QUANTILE APPROXIMATION FOR LARGE-SCALE EARTH OBSERVATION USING T-DIGEST
Michael Engel, Technical University of Munich, Germany; Adrián Luna Cobos, European Union Satellite Centre, Spain; Matej Batic, Sinergise Laboratory for Geographical Information Systems LTD., Slovenia; Marco Körner, Technical University of Munich, Germany
TU4.R14.4: BLIND SINGLE IMAGE-BASED THIN CLOUD REMOVAL USING CLOUD PERCEPTION INTEGRATED FOURIER CONVOLUTION NETWORK
Yujun Guo, Wei He, Hongyan Zhang, Wuhan University, China
TU4.R14.5: A MACHINE LEARNING APPROACH FOR HIGH RESOLUTION FRACTIONAL VEGETATION COVER ESTIMATION USING PLANET CUBESAT AND RGB DRONE DATA FUSION
Jacob Nesslage, Brittany Lopez Barreto, Adam Weingram, Erin Hestir, University of California, Merced, United States
Resources
No resources available.