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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Vydané v:	ISPRS journal of photogrammetry and remote sensing Ročník 132; s. 88 - 101
Hlavní autori:	Rivera-Caicedo, Juan Pablo, Verrelst, Jochem, Muñoz-Marí, Jordi, Camps-Valls, Gustau, Moreno, José
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2017
Predmet:	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
On-line prístup:	Získať plný text
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