## 4 Discussion
The performance evaluation results of the sigmoid calibration function in reconfiguring spectral radiance properties to the pothole surface revealed AUC values of 0.53, 0.35, 0.71, 0.19 and 0.35 for blue, green, red, NIR and red edge spectral channels, respectively. The ROC evaluates the model performance based on the AUC values that range between 0 and 1. For a model to be acceptable, the AUC value must be at least 0.5, with a value closer to 0 indicating a perfect model [58]. The ROC results of the sigmoid function evaluation-produced focus was based on the Area Under Curve (AUC), and the sigmoid calibration model developed based on red channel had AUC value of 0.71, which was the better performance in comparison with the sigmoid calibration models developed based on other spectral channels of the UAV imagery. This is due to the fact that when asphalt materials peel off, the spectral reflectance patterns change to the soil beneath asphalt, and the fact that soil type and brightness substantially influence the reflectance in the red channel of spectrum [59]. Particularly, the deployment of relative operating characteristics (ROC) graphs is progressively being commended for performance evaluation of classifiers [60]. Subsequently, the spectral reflectance of potholes from each spectral channel generated through the prediction by the sigmoid calibration model was compared with that of potholes generated from the original spectral channels of the UAV imagery, and all the original spectral channels were more reflective compared to the predicted spectral channels. This is related to the adjustment of high spectral reflectance, such as soil particles scattered around potholes, that tend to cause noise. However, the ability of the absolute value of spectral band to determine the shape of the sigmoid function depends on the spectral properties of the image to be reconfigured. In other spectral bands, road marks (painted in white) were recognized as potholes due to similarity in spectral reflectance properties. [PERSON] and [PERSON] [61] noted that when a single threshold is generated from the calibration process, the classification error produced in ROC is insignificant. Moreover, all modeling methods perform under specified assumptions, some of which may not hold in a given application, leading to miscalibration [62]. However, the shape of sigmoid function can be restrictive, and it often cannot produce well-calibrated probabilities when the instances are distributed in feature space in a biased fashion (e.g., at the extremes, or all near a separating hyper plane) [62]. Segmenting image is considered a critical component of image processing and analysis, but it can be very difficult, especially on high-resolution remote sensing images, because different landscape features can have similar spectral properties and the same landscape feature can exhibit varying spectral characteristics [63]. Ultimately, it is challenging to identify the ideal threshold value for a scene or landscape, suggesting that user involvement is necessary [64]. However, it can be assumed that nearby pixels often have the same class label (smoothness assumption) based on the spatial autocorrelations among those pixels [65]. Determining the best thresholding algorithm is not the only conclusion that can be drawn from the results. While threshold-based segmentation works well on uniform backgrounds, it faces limitations in situations where landscape features are subjected to varying illumination conditions. Threshold value definition is based on the pixel intensity of the image, where pixels whose intensity values below threshold are assigned to region 1, and the other pixels are assigned to region 2 [66]. We can easily include extraneous pixels that are not part of the desired region, and we can just as easily miss isolated pixels within the region (especially near the boundaries of the region). These effects get worse as the noise gets worse, simply because it's more likely that a pixel's intensity does not represent the normal intensity in the region.