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  5. Aerial remote sensing hyperspectral techniques for rocky outcrops mapping
 
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Aerial remote sensing hyperspectral techniques for rocky outcrops mapping

Author(s)
Filizzola, C.  
Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, Tito (PZ), Italy.  
Pergola, N.  
Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, Tito (PZ), Italy.  
Pignatti, S.  
Istituto di Metodologie Avanzate di Analisi Ambientale, CNR, Tito (PZ), Italy.  
Tramutoli, V.  
Dipartimento di Ingegneria e Fisica dell'Ambiente, Università della Basilicata, Potenza, Italy.  
Date Issued
2002
Issue/vol(year)
2/45 (2002)
Language
English
Subjects
04. Solid Earth::04.02. Exploration geophysics::04.02.05. Downhole, radioactivity, remote sensing, and other methods  
URI
https://www.earth-prints.org/handle/2122/668
Subjects

hyperspectral sensor ...

airborne remote sensi...

robust techniques

geological mapping

Abstract
In this work the MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) hyperspectral data, acquired during aerial campaigns made in 1998 over the Pollino National Park in the framework of the «Progetto Pollino», have been used to set up a supervised technique devoted to identify the presence of selected rocky outcrops. Tests have been performed over an extended area characterised by a complex orography. Within this area, serpentinite was chosen as a test-rock because it is present in isolated outcrops, distributed all over the test-area, besides subtending important problems of environmental nature as it contains asbestos. Geological information, coming from field observations or geological maps, was used to trigger the algorithms and as ground truth for its validation. Two spectral analysis techniques, SAM (Spectral Angle Mapper) and LSU (Linear Spectral Unmixing), have been applied and their results n combined to automatically identify serpentinite outcrops and, in some cases, to mark its boundaries. The approach used in this work is characterised by simplicity (no atmosphere and illumination corrections were performed on MIVIS data), robustness (material of interest is identified for certainty) and intrinsic exportability (the method proposed can be applied on different geographic areas and, in theory, to identify any kind of material because no datum about atmospheric and illumination conditions is required).
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