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Spectral band selection for vegetation properties retrieval using Gaussian processes regression
Affiliation:1. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands;2. School of Mathematical and Geospatial Sciences, RMIT University, GPO Box 2476, Melbourne, Victoria 3001, Australia;1. Irstea, UMR ITAP, 361 rue J.F. Breton, 34196 Montpellier, France;2. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia;3. Ophthalmology, University of Melbourne, Department of Surgery, Melbourne, Australia;4. Irstea, UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, 34093 Montpellier, France;1. Department of Earth & Environment, Boston University, Boston, MA, USA;2. Environmental & Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA;3. Sustainability Studies, Stony Brook University, Stony Brook, NY, USA;4. Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA
Abstract:
Keywords:Gaussian processes regression (GPR)  Machine learning  Band selection  ARTMO  Vegetation properties  Hyperspectral
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