TU1.R10.4

DEEP LEARNING MODELS USING MULTI-MOIDAL REMOTE SENSING FOR PREDICTION OF MAIZE YIELD IN PLANT BREEDING EXPERIMENTS

Claudia Aviles Toledo, Melba Crawford, Purdue University, United States
Session:
TU1.R10: Advances in Multimodal Remote Sensing Image Processing and Interpretation I Oral
Track:
Community-Contributed Sessions
Location:
Room 101
Presentation Time:
Tue, 18 Jul, 09:06 - 09:18 Pacific Time (UTC -8)
Session Co-Chairs:
Lexie Yang, Oak Ridge National Laboratory and Gulsen Taskin, Istanbul Tecnical University
Session Manager:
Shan 0
Presentation
Not logged in.
Discussion
Not logged in.
Resources
No resources available.
Session TU1.R10
TU1.R10.1: UNSUPERVISED BUILDING CHANGE DETECTION IN MULTI-MODAL SAR IMAGES USING CYCLEGAN
Luca Bergamasco, Francesca Bovolo, Fondazione Bruno Kessler, Italy
TU1.R10.2: Extended Vision Transformer for Land Use and Land Cover Classification
Jing Yao, Danfeng Hong, Aerospace Information Research Institute, Chinese Academy of Sciences, China; Chenyu Li, School of Mathematics, Southeast University, China; Jocelyn Chanussot, Univ. Grenoble Alpes, France
TU1.R10.3: EXPLORING SIAMESE REPRESENTATION LEARNING FOR MULTIMODAL SATELLITE IMAGE TIMES SERIES
Silvia Valero, Univ. Toulouse 3 / CESBIO, France; Charlotte Pelletier, Univ. Bretagne Sud / IRISA, France; Iris Dumeur, CESBIO, France; Jordi Inglada, CESBIO / CNES, France
TU1.R10.4: DEEP LEARNING MODELS USING MULTI-MOIDAL REMOTE SENSING FOR PREDICTION OF MAIZE YIELD IN PLANT BREEDING EXPERIMENTS
Claudia Aviles Toledo, Melba Crawford, Purdue University, United States
TU1.R10.5: THE USE OF MULTIMODAL REMOTE SENSING DATA FOR NASA’S ADVANCED INFORMATION SYSTEMS
Jacqueline Le Moigne, NASA, United States
Resources
No resources available.