http://www.cnr.it/ontology/cnr/individuo/prodotto/ID218452

Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use (Articolo in rivista) (literal)

Anno

2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

10.1109/JSTARS.2013.2255981 (literal)

Alternative label

Amato, U.a, Antoniadis, A.b, Carfora, M.F.a , Colandrea, P.c, Cuomo, V.d, Franzese, M.a, Pignatti, S.d, Serio, C.e (2013)
Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use
in IEEE journal of selected topics in applied earth observations and remote sensing (Print); IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE,, PISCATAWAY, NJ 08855-4141 (Stati Uniti d'America)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Amato, U.a, Antoniadis, A.b, Carfora, M.F.a , Colandrea, P.c, Cuomo, V.d, Franzese, M.a, Pignatti, S.d, Serio, C.e (literal)

Pagina inizio

615 (literal)

Pagina fine

625 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6495492 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

6 (literal)

Rivista

IEEE journal of selected topics in applied earth observations and remote sensing (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

11 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

2 (literal)

Note

ISI Web of Science (WOS) (literal)
Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

a Istituto per le Applicazioni Del Calcolo Mauro Picone CNR, Napoli, Italy b Institute J. Kuntzmann, Université Joseph Fourier, Grenoble, France c Compagnia Generale per Lo Spazio S.p.A., Milano, Italy d Istituto di Metodologie per Analisi Ambientale CNR, Tito Scalo (Potenza), Italy e Dipartimento di Ingegneria e Fisica Ambientale, Università della Basilicata, Potenza, Italy (literal)

Titolo

Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use (literal)

Abstract

The upcoming launch of the next generation of hyperspectral satellites (PRISMA, EnMap, HyspIRI, etc.) will meet the increasing demand for the availability/accessibility of hyperspectral information on agricultural land use from the agriculture community. To this purpose, algorithms for the classification of remotely sensed images are here considered for agricultural monitoring of cultivated area, exploiting remotely sensed high spectral resolution images. Classification is accomplished by procedures based on discriminant analysis tools that well suit hyperspectrality, circumventing what in statistics is called \"the curse of dimensionality\". As a byproduct of classification, a full assessment of the spectral bands of the sensor is obtained, ranking them with the purpose of understanding their role in segmentation and classification. The methodology has been validated on two independent image datasets gathered by the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) sensor for which ground validations were available. A comparison with the popular multiclass SVM (Support Vector Machines) classifier is also presented. Results show that a good classification (minimum global success rate 95% through all experiments) is achieved by using the 10 spectral bands selected as the most discriminant by the proposed procedure; moreover, it also appears that nonparametric techniques generally outperform parametric ones. The present study confirms that the new generation of hyperspectral satellite data like PRISMA can ripen an end-user application for agricultural land-use of cultivated area. (literal)

Editore