Hyperdeep: Comparison of Ai-Based Methods for Predicting Chemical Components in Hyperspectral Images
Author:
Affiliation:
1. University of Castilla-La Mancha,ETSI Industrial, VISILAB,Ciudad Real,Spain
Funder
Junta de Comunidades de Castilla-La Mancha
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9897158/9897159/09897549.pdf?arnumber=9897549
Reference9 articles.
1. BENCHMARKING DEEP LEARNING FRAMEWORKS FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE MULTISPECTRAL DATA
2. Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review
3. Towards On-Board Hyperspectral Satellite Image Segmentation: Understanding Robustness of Deep Learning through Simulating Acquisition Conditions
4. Intuition-1: Processing of hyperspectral images in orbit,0
5. Field-level crop yield estimation with PRISMA and Sentinel-2
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