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Remote Sens. 2014, 6(4), 2765-2781;

Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation

Institute of Atmospheric Pollution Research, National Research Council (CNR), Area della Ricerca di Roma 1, Via Salaria Km 29,300 Monterotondo, I-00015 Rome, Italy
Department of Civil, Construction, and Environmental Engineering, Sapienza—University of Rome, Via Eudossiana 18, I-00184 Rome, Italy
Author to whom correspondence should be addressed.
Received: 20 December 2013 / Revised: 15 February 2014 / Accepted: 17 March 2014 / Published: 26 March 2014
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The ability to classify asphalt surfaces is an important goal for the selection of suitable non-variant targets as pseudo-invariant targets during the calibration/validation of remotely-sensed images. In addition, the possibility to recognize different types of asphalt surfaces on the images can help optimize road network management. This paper presents a multi-resolution study to improve asphalt surface differentiation using field spectroradiometric data, laboratory analysis and remote sensing imagery. Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) airborne data and multispectral images, such as Quickbird and Ikonos, were used. From scatter plots obtained by field data using λ = 460 and 740 nm, referring to MIVIS Bands 2 and 16 and Quickbird and Ikonos Bands 1 and 4, pixels corresponding to asphalt covering were identified, and the slope of their interpolation lines, assumed as asphalt lines, was calculated. These slopes, used as threshold values in the Spectral Angle Mapper (SAM) classifier, obtained an overall accuracy of 95% for Ikonos, 98% for Quickbird and 93% for MIVIS. Laboratory investigations confirm the existence of the asphalt line also for new asphalts, too. View Full-Text
Keywords: asphalt; Spectral Angle Mapper; MIVIS; Quickbird; Ikonos asphalt; Spectral Angle Mapper; MIVIS; Quickbird; Ikonos

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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Mei, A.; Salvatori, R.; Fiore, N.; Allegrini, A.; D'Andrea, A. Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation. Remote Sens. 2014, 6, 2765-2781.

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