G
G. Trianni
Researcher at University of Pavia
Publications - 29
Citations - 2260
G. Trianni is an academic researcher from University of Pavia. The author has contributed to research in topics: Hyperspectral imaging & Image resolution. The author has an hindex of 11, co-authored 29 publications receiving 2070 citations. Previous affiliations of G. Trianni include Serco Group.
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Journal ArticleDOI
Recent Advances in Techniques for Hyperspectral Image Processing
Antonio Plaza,Jon Atli Benediktsson,Joseph W. Boardman,J. Brazile,Lorenzo Bruzzone,Gustavo Camps-Valls,Jocelyn Chanussot,Mathieu Fauvel,Mathieu Fauvel,Paolo Gamba,Anthony J. Gualtieri,Mattia Marconcini,James C. Tilton,G. Trianni +13 more
TL;DR: A seminal view on recent advances in techniques for hyperspectral image processing, focusing on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information.
Journal ArticleDOI
Multitemporal settlement and population mapping from Landsat using Google Earth Engine
Nirav N. Patel,Emanuele Angiuli,Paolo Gamba,Andrea E. Gaughan,Gianni Lisini,Forrest R. Stevens,Andrew J. Tatem,Andrew J. Tatem,G. Trianni +8 more
TL;DR: Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.
Journal ArticleDOI
Rapid Damage Detection in the Bam Area Using Multitemporal SAR and Exploiting Ancillary Data
TL;DR: It is shown that the combination of intensity and phase features enhances the damage pattern extracted from the data temporal stack using a spatially aware classifier, and the use of ancillary data further improves the accuracy.
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Improved VHR Urban Area Mapping Exploiting Object Boundaries
TL;DR: Experimental results on hyperspectral and satellite VHR imagery show the superior performance of this method over conventional NN and MRF classifiers.
Journal ArticleDOI
Urban Mapping in Landsat Images Based on Normalized Difference Spectral Vector
Emanuele Angiuli,G. Trianni +1 more
TL;DR: The experiments presented in this letter show the effectiveness of the proposed technique in detecting urban areas in extremely different environments, and the NDSV+SAM approach has provided the best results, with an overall accuracy of 97%.