## 4 Discussion  
The significance of timely and accurate detection of potholes for maintenance has long been emphasized [44]. In recent years, studies have attempted to segment large scale images for feature recognition [45, 46]. However, detection and characterization of potholes from multispectral data without reconfiguring radiance properties to pothole surface may lead to inaccurate determination of pothole size [47]. The study was aimed at calibrating radiometric properties of UAV multispectral imagery to pothole features based on sigmoid calibration function embedded in MLP algorithm. In most cases, the detection of potholes is done manually by inspection, with GPS, recording the location of the pothole at a precision of at most 3 m. However, the accuracy of the GPS location at this precision is questionable, especially for typical roads with a width of about 8 m. In the quest for accurate timely detection of potholes, remote sensing has been endorsed due to its reasonable temporal resolution [48]. In the quest for improved detection of potholes using spectral unmixing of Sentinel-2 raw imagery, [PERSON] [49] obtained low accuracy, and this could be associated with the inability of Sentinel-2 sensor in detecting small possible features. Recent studies highlighted the significance of UAV imagery in predicting potholes [50], due to its ability to collect small features of a few centimeters size. UAV-based remote sensing, in particular, has been deemed a suitable option for offering high-spatial resolution images that are capable of detecting potholes. [PERSON] et al. [51] noted that, though ultra-spatial-resolution Unmanned Aerial Vehicle sensors are capable of retrieving multiple forms of landscape information, they are prone to issues related to classification due to noise caused by the presence of smallest possible detectable features. As such, optimized recognition of features from these sensors based on a single or multiple goodness-of-fit numerical approach is required [52]. The sigmoid calibration function algorithm facilitated a successful reconfiguration of spectral radiance properties of UAV imagery to the pothole surface. The presence of the sigmoid function in MLP allows this  
Figure 9: The area of potholes; (**a**) pothole 1, (**b**) pothole 2 and (**c**) pothole 3.  
algorithm to learn abstract features from raw inputs, which is useful for many applications, such as image recognition [34]. A more reliable optimization function must take measurement of uncertainties into account [53]. In this investigation, the reliability of the results obtained using the sigmoid calibration function were evaluated by employing ROC area under curve. Although validation approaches such as the area under margin (AUM) are also capable of evaluating the performance of sigmoid calibration function, this technique was noted to lack the ability to account for account model confidence [53]. The area-under-the-ROC-curve approach has been extensively deployed in the pattern recognition domain [54]. Using this approach, thresholds can also be derived for optimal adaptation to a change in class prior. [PERSON] and [PERSON] [55] noted that ROC-AUC approach can optimize reconfiguration outcomes even when the classifier is poorly calibrated. Generally, pothole surface was noted to have a higher spectral reflectance than asphalt surface in all spectral channels but blue channels. In their analysis of field-based measurements of asphalt road spectra, [PERSON] et al. [56] noted that asphalt surfaces under varying aging conditions showed relatively diverse spectral reflectance patterns. [PERSON] et al. [57] noted that spectral reflectance in the visible-infrared (VNIR) spectrum increases with the increase in tarred road age. The sigmoid calibration function results revealed its optimization ability lying on the red spectral channel of UAV imagery. The calibrated spectral channels characterized the pothole surface based on the spectral values that were generally lower than those of the original spectral channel.