Bitumen Removal Determination on Asphalt Pavement Using Digital Imaging Processing and Spectral Analysis

DOI: 10.4236/ojapps.2014.46034   PDF   HTML     3,707 Downloads   5,358 Views   Citations


This research aims to define an efficient and fast quantification of bitumen removal on the road surface by Digital Imaging Processing (DIP) and spectral analysis. The retrieval of bitumen removal is an important issue for road management and environmental studies related to asphalt wear and environmental pollution. The calculation of the Exposed Aggregate Index (EAI), based on DIP, allows to quantify in each frame the superficial removal of bitumen and the exposure of aggregates. A procedure, based on non-parametric classification process of digital images, gives a fast response of EAI. A correlation among EAI and spectral data, between 390 nm and 900 nm range, is evaluated. Results show a good correlation between spectral data at different wavelength and EAI. Finally, this work evaluates the possibility to retrieve asphalt bitumen removal through remote sensed imagery.

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Mei, A. , Manzo, C. , Bassani, C. , Salvatori, R. and Allegrini, A. (2014) Bitumen Removal Determination on Asphalt Pavement Using Digital Imaging Processing and Spectral Analysis. Open Journal of Applied Sciences, 4, 366-374. doi: 10.4236/ojapps.2014.46034.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Andreou, C., Karathanassi, V. and Kolokoussis P. (2011) Investigation of Hyperspectral Remote Sensing for Mapping Asphalt Road Conditions. International Journal of Remote Sensing, 32, 6315-6333.
[2] Villa, P., Boschetti, M., Bianchini, F. and Cella, F. (2012) A Hybrid Approach for Remote Sensing Multi-Temporal Mapping of Urban Areas in Milan Province, Italy. European Journal of Remote Sensing, 45, 333-347.
[3] Bell, C.A. (1989) Summary Report on the Aging of Asphalt-Aggregate Systems. Strategic Highway Research Program (SHRP) Publications SHRP-A-305, 100 p.
[4] Saoula, S., Soudani, K., Haddadi, S., Munoz, M. and Santamaria, A. (2013) Analysis of the Rheological Behavior of Aging Bitumen and Predicting the Risk of Permanent Deformation of Asphalt. Materials Sciences and Applications, 4, 312-318.
[5] Lindgren, A. (1996) Asphalt Wear and Pollution Transport. The Science of the Total Environment, 189-190, 281-286.
[6] Noronha, V., Herold, M., Gardner, M. and Roberts, D.A. (2002) Spectrometry and Hyperspectral Remote Sensing for Road Centerline Extraction and Evaluation of Pavement Condition. Proceedings of the Pecora Conference, Denver, CO.
[7] Mei, A., Salvatori, R. and Allegrini, A. (2011) Analysis of Paved Areas with Field Data and MIVIS Hyperspectral Images. Italian Journal of Remote Sensing, 43, 147-159.
[8] Mei, A., Fiore, N., Salvatori, R., D’Andrea, A. and Fontana, M. (2012) Spectroradiometric Laboratory Measures on Asphalt Concrete: Preliminary Results. Procedia-Social and Behavioral Sciences, 53, 514-523.
[9] Herold, M., Roberts, D.A., Gardner, M. and Dennison, P. (2004) Spectrometry for Urban Area Remote Sensing-Development and Analysis of a Spectral Library from 350 to 2400 nm. Remote Sensing Environment, 91, 304-319.
[10] Herold, M. and Roberts, D. (2005) Spectral Characteristics of Asphalt Road Aging and Deterioration: Implications for Remote-Sensing Applications. Applied Optics, 44, 4327-4334.
[11] Mei, A. and Salvatori, R. (2013) Urban Mapping Using Ikonos Imagery. International Journal of Remote Sensing & Geoscience, 2, 55-58.
[12] Shvetsov, M. (1954) Concerning Some Additional Aids in Studying Sedimentary Formations. Bulletin Moscow Society Naturalists (Pub. Moscow Univ. Geol. Sect.), 29, 61-66.
[13] Elunai, R., Chandran, V. and Gallagher, E. (2011) Asphalt Concrete Surfaces Macrotexture Determination from Still Images. IEEE Transactions on Intelligent Transportation Systems, 12, 857-869.
[14] Bruno, L., Parla, G. and Celauro, C. (2012) Image Analysis for Detecting Aggregate Gradation in Asphalt Mixture from Planar Images. Construction and Building Materials, 28, 21-30.
[15] Marinoni, N., Pavese, A., Foi, M. and Trombino, L. (2005) Characterization of Mortar Morphology in Thin Sections by Digital Image Processing. Cement and Concrete Research, 35, 1613-1619.
[16] Bessa, I.S., Castelo Branco, V.T.F. and Soares, J.B. (2012) Evaluation of Different Digital Image Processing Software for Aggregates and Hot Mix Asphalt Characterizations. Construction and Building Materials, 37, 370-378.
[17] Priego, B., Souto, D., Bellas, F. and Duro, R.J. (2013) Hyperspectral Image Segmentation through Evolved Cellular Automata. Pattern Recognition Letters, 34, 1648-1658.
[18] Zou, Q., Cao, Y., Li, Q., Mao, Q. and Wang S. (2012). CrackTree: Automatic Crack Detection from Pavement Images. Pattern Recognition Letters, 33, 227-238.
[19] Manzo, C., Mei, A., Salvatori, R., Bassani, C. and Allegrini, A. (2014) Spectral Modeling for Retrieval of Aggregates Index of Asphalted Surfaces and Sensitivity Analysis. Building and Construction Materials, 61, 147-155.
[20] Skoglar, P., Orguner, U., Tornqvist, D. and Gustafsson, F. (2012) Road Target Search and Tracking with Gimballed Vision Sensor on an Unmanned Aerial Vehicle. Remote Sensing, 4, 2076-2111.
[21] Herold, M., Roberts, D., Noronha, V. and Smadi, O. (2008) Imaging Spectrometry and Asphalt Road Surveys. Transportation Research Part C: Emerging Technologies, 16, 153-166.
[22] Jia, J., (1993) Seed Maize Quality Inspection with Machine Vision. Proceedings SPIE Computer Vision for Industry, 1989, 288-295.
[23] Richards, J.A. and Jia, X. (1999) Remote Sensing Digital Image Analysis—An Introduction. 3rd Edition, Springer Verlag, Berlin.
[24] Mei, A., Salvatori, R., Fiore, N., Allegrini, A. and D’Andrea, A. (2014) Integration of Field and Laboratory Spectral Data with Multi-Resolution Remote Sensed Imagery for Asphalt Surface Differentiation. Remote Sensing, 6, 2765-2781.
[25] Congalton, R.G. and Green, K. (1999) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. Lewis Publishers, Boca Raton.

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