Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

ISPRS Journal of Photogrammetry and Remote Sensing

Volumn 132, Issue , 2017, Pages 88-101

  Rivera Caicedo, Juan Pablo ^a,b   Verrelst, Jochem ^a   Muñoz Marí, Jordi ^a   Camps Valls, Gustau ^a   Moreno, José ^a  

^a UNIVERSITY OF VALENCIA   (Spain)

^b Secretary of Research and Postgraduate   (Mexico)

Author keywords

ARTMO; Biophysical parameter retrieval; Hyperspectral; Machine learning regression algorithms; Spectral dimensionality reduction methods; Vegetation properties

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOPHYSICS; COMPUTATIONAL EFFICIENCY; DATA HANDLING; HYPERSPECTRAL IMAGING; LEARNING ALGORITHMS; LEARNING SYSTEMS; REGRESSION ANALYSIS; UNCERTAINTY ANALYSIS; VEGETATION;

ARTMO; BIOPHYSICAL PARAMETER RETRIEVALS; DIMENSIONALITY REDUCTION METHOD; HYPERSPECTRAL; REGRESSION ALGORITHMS; VEGETATION PROPERTIES;

DATA REDUCTION;

AIRBORNE SENSING; ALGORITHM; BIOPHYSICS; DATA PROCESSING; DATA SET; LEAF AREA INDEX; MACHINE LEARNING; MODEL; NUMERICAL METHOD; RADIATIVE TRANSFER; REFLECTANCE; SENSOR; SPECTRAL ANALYSIS; VEGETATION DYNAMICS;

EID: 85029070767     PISSN: 09242716     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.isprsjprs.2017.08.012     Document Type: Article

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