DocumentCode :
2692855
Title :
From Multi-Spectral to Hyper-Spectral Imagery: a Quantitative Analysis of the Improvements in Terms of Land Cover Classification
Author :
Duca, R. ; Del Frate, F. ; Roca, F. Gascon
Author_Institution :
Dipt. di Inf., Sist. e Produzione, Tor Vergata Univ., Rome
Volume :
4
fYear :
2008
fDate :
7-11 July 2008
Abstract :
In this paper we report on the different land cover classification performances, over the same urban and rural landscape, of Multi-spectral Landsat ETM and Hyperspectral CHRIS-PROBA instruments. A Landsat ETM+ acquisition of 19 August 2004 and a CHRIS Proba image of 19 August 2006 over the same test area of Frascati-Tor Vergata have been considered. Even though a time shift of two years is present, the images have been acquired in the same day of the year, which makes them sufficiently suitable for the comparison. For the classification task a neural network algorithm is considered for both type of images. Indeed neural algorithms in last years have demonstrated at the same time a particular ease on managing a large domain of inputs and an important effectiveness in performing the decision task for the classification of remotely sensed images. A quantitative analysis in terms of classification accuracy on the different types of surfaces is carried out and the results critically analysed.
Keywords :
image classification; neural nets; remote sensing; AD 2004 08 19; AD 2006 08 19; Frascati-Tor Vergata; Hyperspectral CHRIS-PROBA instruments; Multispectral Landsat ETM acquisition; land cover classification; neural network algorithm; remotely sensed images; rural landscape; surface types; urban landscape; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Instruments; Neural networks; Remote sensing; Satellites; Sensor phenomena and characterization; Space technology; Spatial resolution; Land cover maps; hyperspectral imagery; multi-spectral; neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
Type :
conf
DOI :
10.1109/IGARSS.2008.4779836
Filename :
4779836
Link To Document :
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