Mapping asphalt pavement aging and condition using multiple endmember spectral mixture analysis in Beijing, China

Abstract

Abstract. Asphalt road reflectance spectra change as pavement ages. This provides the possibility for remote sensing to be used to monitor a change in asphalt pavement conditions. However, the relatively narrow geometry of roads and the relatively coarse spatial resolution of remotely sensed imagery result in mixtures between pavement and adjacent landcovers (e.g., vegetation, buildings, and soil), increasing uncertainties in spectral analysis. To overcome this problem, multiple endmember spectral mixture analysis (MESMA) was used to map the asphalt pavement condition using Worldview-2 satellite imagery in this study. Based on extensive field investigation and in situ measurements, aged asphalt pavements were categorized into four stagespreliminarily aged, moderately aged, heavily aged, and distressed. The spectral characteristics in the first three stages were further analyzed, and a MESMA unmixing analysis was conducted to map these three kinds of pavement conditions from the Worldview-2 image. The results showed that the road pavement conditions could be detected well and mapped with an overall accuracy of 81.71% and Kappa coefficient of 0.77. Finally, a quantitative assessment of the pavement conditions for each road segment in this study area was conducted to inform road maintenance management.

RETRACTED: Identifying Asphalt Pavement Distress Using UAV LiDAR Point Cloud Data and Random Forest Classification

“…That is, the thermal radiation information will change based on the pavement conditions. Therefore, previous research has focused on obtaining the spectral data of target sections using multispectral or hyperspectral sensors and then evaluating pavement usage status by comparing and analyzing the spectral features of asphalt pavement at different ages [25]. For example, Mei et al analyzed field and laboratory data with the goal of differentiating asphalted surfaces.…”

“…The analysis of the in situ measured spectra of asphalt road pavement showed that asphalt pavements in different aging conditions exhibit quite different spectral patterns over the range of 0.35 to 2.5 µm. The assessment of the analysis results showed that the method is capable of classifying road pavements with an overall accuracy of 81.71% and Kappa coefficient of 0.77, when the in situ data were used as a reference [25]. However, there are many disadvantages to identifying asphalt pavement distress using remotely sensed imagery, such as the high cost of data acquisition, the long data acquisition cycles, and the use of remote sensors that are not sufficiently flexible.…”

“…However, there are many disadvantages to identifying asphalt pavement distress using remotely sensed imagery, such as the high cost of data acquisition, the long data acquisition cycles, and the use of remote sensors that are not sufficiently flexible. In addition, because of the spectral similarity among different types of pavement distress, it is difficult to obtain accurate results by only relying on spectral features [25,26].…”

“…UAV is a flexible platform that can be configured with various remote sensing sensors, in which digital images are the most commonly used data type. Several methods [25,26,[32][33][34][35] that are based on digital image processing and machine learning algorithms have been proposed for pavement distress detection, using high-resolution images that were acquired from UAV platforms. For example, Kim utilized a simple UAV system to capture the pavement images and detected cracks based on the image binarization method [32].…”

Object-Based and Supervised Detection of Potholes and Cracks From the Pavement Images Acquired by Uav

“…The quality of pavement has a close relationship with the lifetime of road (Pan et al, 2017). In a general, because of the combined effect of aging and deterioration of road surface, some kinds of distresses would always appear on the pavement finally.…”