Start Date
10-3-2017 2:00 PM
End Date
10-3-2017 3:30 PM
Abstract Text
Background:
Although the airborne platform is efficient and economic compared to the popular ground vehicles in road condition survey (RCS), studies on applying it in RCS are limited. A former study on airborne hyperspectral imagery (AHI) tried to tie a band ratio to pavement condition index. Its result proved the value in AHI, yet pointed out the difficulty in quantitative evaluation.
Methods:
This study further explored the application of AHI in RCS, and fully took advantage of the entire spectrum shape, rather than a ratio. Aiming at the cracking area percentage (CAP) on the asphalt paved arterial road system in the City of Surrey, BC, the studied AHI was used to build a road CAP spectral library (SLib). The SLib contains five road classes separately with 0~1%, 2~5%, 6~10%, 11~30%, and 31~100% CAP. Then the study selects arterial roads in ten locations covering ~20 sq km to classify using the SLib.
Results:
The selected spectra well depict the reflectance increase from newer roads with less CAP to older roads with more CAP. But, the accuracies of the classification are only ~20%. By combining the first three classes and last two classes, the classification accuracy grows approximately 10~60% depending on the test tiles.
Discussion & conclusion:
Two conclusions are made: (1) the great misclassification among neighbor classes, e.g. class 0~1%, 2~5%, and 6~10, as well as 11~30% and 31~100% are often misclassified with each other; and (2) the result tends to overestimate the CAP.
Interdisciplinary reflection:
The engineering survey and geographic analysis contributed in extracting CAP from AHI. Its result is instructional in urban modeling and planning.
Included in
P10. Road Cracking Area Percentage Evaluation Using Airborne Hyperspectral Imagery
Background:
Although the airborne platform is efficient and economic compared to the popular ground vehicles in road condition survey (RCS), studies on applying it in RCS are limited. A former study on airborne hyperspectral imagery (AHI) tried to tie a band ratio to pavement condition index. Its result proved the value in AHI, yet pointed out the difficulty in quantitative evaluation.
Methods:
This study further explored the application of AHI in RCS, and fully took advantage of the entire spectrum shape, rather than a ratio. Aiming at the cracking area percentage (CAP) on the asphalt paved arterial road system in the City of Surrey, BC, the studied AHI was used to build a road CAP spectral library (SLib). The SLib contains five road classes separately with 0~1%, 2~5%, 6~10%, 11~30%, and 31~100% CAP. Then the study selects arterial roads in ten locations covering ~20 sq km to classify using the SLib.
Results:
The selected spectra well depict the reflectance increase from newer roads with less CAP to older roads with more CAP. But, the accuracies of the classification are only ~20%. By combining the first three classes and last two classes, the classification accuracy grows approximately 10~60% depending on the test tiles.
Discussion & conclusion:
Two conclusions are made: (1) the great misclassification among neighbor classes, e.g. class 0~1%, 2~5%, and 6~10, as well as 11~30% and 31~100% are often misclassified with each other; and (2) the result tends to overestimate the CAP.
Interdisciplinary reflection:
The engineering survey and geographic analysis contributed in extracting CAP from AHI. Its result is instructional in urban modeling and planning.