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2015 Contributo in Atti di convegno metadata only access

Environmental products overview of the Italian hyperspectral prisma mission: The SAP4PRISMA project

S Pignatti ; N Acito ; U Amato ; R Casa ; F Castaldi ; R Coluzzi ; R De Bonis ; M Diani ; V Imbrenda ; G Laneve ; S Matteoli ; A Palombo ; S Pascucci ; F Santini ; T Simoniello ; C Ananasso ; G Corsini ; V Cuomo

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

Hyperspectral imaging Vegetation mapping Agriculture SAP4PRISMA PRISMA mission

CNR IRIS

2013 Articolo in rivista metadata only access

Statistical classification for assessing PRISMA hyperspectral potential for agricultural land use

Amato ; Ua ; Antoniadis ; Ab ; Carfora ; MFa ; Colandrea ; Pc ; Cuomo ; Vd ; Franzese ; Ma ; Pignatti ; Sd ; Serio ; Ce

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.

discriminant analysis Hyperspectral data independent components land use.

CNR IRIS

2008 Articolo in rivista metadata only access

Statistical cloud detection from SEVIRI multispectral images

Amato U ; Antoniadis A ; Cuomo V ; Cutillo L ; Franzese M ; Murino L ; Serio C

Cloud detection from geostationary satellite multispectral images through statistical methodologies is investigated. Discriminant analysis methods are considered to this purpose, endowed with a nonparametric density estimation and a linear transform into principal and independent components. The whole methodology is applied to the MSG-SEVIRI sensor through a set of test images covering the central and southern part of Europe. "Truth" data for the learning phase of discriminant analysis are taken from the cloud mask product MOD35 in correspondence of passages of MODIS close to the SEVIRI images. Performance of the discriminant analysis methods is estimated over sea/land, daytime/nighttime both when training and test datasets coincide and when they are different. Discriminant analysis shows very good performance in detecting clouds, especially over land. PCA and ICA are effective in improving detection.

clouds multispectral classification geostationary MSG

CNR IRIS