Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties...

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Bibliographic Details
Published in:	ISPRS journal of photogrammetry and remote sensing Vol. 132; pp. 88 - 101
Main Authors:	Rivera-Caicedo, Juan Pablo, Verrelst, Jochem, Muñoz-Marí, Jordi, Camps-Valls, Gustau, Moreno, José
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.10.2017
Subjects:	algorithms artificial intelligence ARTMO automation Biophysical parameter retrieval data collection Hyperspectral image analysis information processing leaf area index Machine learning regression algorithms normal distribution reflectance regression analysis seeds Spectral dimensionality reduction methods spectrometers uncertainty vegetation Vegetation properties Biophysical parameter retrieval Spectral dimensionality reduction methods Machine learning regression algorithms Vegetation properties Hyperspectral ARTMO
ISSN:	0924-2716, 1872-8235
Online Access:	Get full text
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