Abstract—The small hyperspectral imager Compact High-Resolution Imaging Spectrometer (CHRIS) is the most important instrument for Earth observation included in the payload of the European Space Agency Third-Part Mission Project for On-Board Autonomy (PROBA)-1 satellite. This instrument has provided dozens of images in several target areas in the world, and a good number of acquisitions are available for the test site of Frascati and Tor Vergata, Italy. This paper reports several results concerning the generation of thematic maps obtained from CHRIS mode-3 imagery. The potential of the use of different configurations for the input vector exploiting multispectral, multiangular, and multitemporal measurements has been investigated, and the results have been evaluated and compared in terms of accuracy in the classification. The core of the decision task has been developed using the neural network methodology. Indeed, this approach is characterized by a particular ease in performing nonlinear mapping of a multidimensional set of inputs into the output one.

Duca, R., DEL FRATE, F. (2008). Hyperspectral and multi-angle CHRIS proba images for the generation of land cover maps. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 46(10), 2857-2866 [10.1109/TGRS.2008.2000741].

Hyperspectral and multi-angle CHRIS proba images for the generation of land cover maps

DEL FRATE, FABIO
2008-01-01

Abstract

Abstract—The small hyperspectral imager Compact High-Resolution Imaging Spectrometer (CHRIS) is the most important instrument for Earth observation included in the payload of the European Space Agency Third-Part Mission Project for On-Board Autonomy (PROBA)-1 satellite. This instrument has provided dozens of images in several target areas in the world, and a good number of acquisitions are available for the test site of Frascati and Tor Vergata, Italy. This paper reports several results concerning the generation of thematic maps obtained from CHRIS mode-3 imagery. The potential of the use of different configurations for the input vector exploiting multispectral, multiangular, and multitemporal measurements has been investigated, and the results have been evaluated and compared in terms of accuracy in the classification. The core of the decision task has been developed using the neural network methodology. Indeed, this approach is characterized by a particular ease in performing nonlinear mapping of a multidimensional set of inputs into the output one.
2008
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore ING-INF/02 - CAMPI ELETTROMAGNETICI
English
Con Impact Factor ISI
Hyperspectral, land cover, neural networks
Duca, R., DEL FRATE, F. (2008). Hyperspectral and multi-angle CHRIS proba images for the generation of land cover maps. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 46(10), 2857-2866 [10.1109/TGRS.2008.2000741].
Duca, R; DEL FRATE, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2108/38707
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