Remote sensing techniques are used on asphalt pavements to study their physical and chemical characteristics, and also to evaluate the condition of pavements such as aging and material composition. There are various techniques in remote sensing that can be used for the study of asphalt concrete’s behavior, including spectral libraries, Unmanned Aerial Vehicles (UAVs), digital sensors (such as RGB and thermal imaging), Ground Penetrating Radars (GPRs), and satellite data. The most common equipment to be used for ground truth data is a spectroradiometer. Spectroradiometric ground data can be used to understand the spectral behavior (i.e., spectral signatures) of the asphalt pavements, as this is also obtained through satellites. There is a variety of spectroradiometers to be used depending on its use (i.e., the desired spectral wavelength), but reliable ones can be considered expensive.
An effort was made to reduce the cost of scientific laboratory equipment, make remote sensing research more understandable and accessible to the industry by introducing colorimeters for the characterization of asphalt pavements. The science of colorimetry demonstrates that the color of an object depends on the spectral composition of the incident light, the object’s spectral reflectance or transmittance, the observer’s spectral response, and the illuminating and viewing geometry.
Materials and Methods
Step 1: The methodology followed in this study concerned the correlation of the data produced between the two main equipment used, i.e., spectroradiometer and colorimeter. The spectroradiometer produces spectral data, while the colorimeter produces color data (i.e., Lab or RGB). Thus, a two-way comparison between the equipment was used to provide an alternative way to analyze color/spectral data. In other words, after the collection of data.
Step 2: The equipment datasets were compared to each other, firstly, by converting the spectroradiometer data into color data.
Step 3: Data analysis took place using statistical tests such as T-Test, Analysis of Variance (ANOVA) test, and Pearson correlation.
Step 4: When the data of the two equipment was proved to be statistically related, the second (vise-versa) conversion took place, i.e., the color data were converted into spectral data.
Step 5: An extra step of data analysis took place. But this time the spectral data for both equipment went through in-band analysis of WorldView 3 (WV3) and were compared using two separability indices (Euclidean Distance and Mahalanobis Distance).
Step 6: By applying the results of the methodology, there is a clear characterization of asphalt pavements in terms of their ageing.
Resources
Data collection was performed using two main equipment, a color spectrophotometer, and pocket colorimeter.
Results and Discussion
The results showed that there is a downgrading of parameter lightness by using the colorimeter data, but in general, all the data are in line with literature since it shows that the L* for both equipment (Handheld colorimeter and colorimeter) increases as the age of the asphalt pavement increases as well. In addition, statistics were used to confirm that the age category of asphalt pavements cannot be mismatched using both pieces of equipment. Statistically, the colorimeter data proved to comply with the spectroradiometer data.
When spectral data were compared for both equipment using six multispectral bands of WV3 satellite, the outcomes showed consistency for the two equipment used. The separability indices (Euclidean, Mahalanobis distances) were in line for both types of equipment for the age categorization of asphalt pavements. As far as WV3 is concerned, proved to be the most appropriate separability index for age characterization of asphalt pavements for colorimeter as spectroradiometer data. As a synopsis, a colorimeter can be used for the calibration of satellite data since its data are in line with spectrometric data.