Integrating Sigmoid Calibration Function into Entropy Thresholding Segmentation for Enhanced Recognition of Potholes Imaged Using a UAV Multispectral Sensor  
[PERSON]  
1 Department of GIS and Remote Sensing, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa; [EMAIL_ADDRESS] (S.N. ); [EMAIL_ADDRESS] (A.S.)  
[PERSON]  
2 Agricultural Research Council, Natural Resource and Engineering (ARC-NRE), Pretoria 0001, South Africa; [EMAIL_ADDRESS]  
[PERSON]  
1 Department of GIS and Remote Sensing, University of Fort Hare, Private Bag X1314, Alice 5700, South Africa; [EMAIL_ADDRESS] (A.S.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/pdf/10.330/appl/10](https://www.mdpi.com/journals/pdf/pdf/10.330/appl/10)]([https://www.mdpi.com/journals/pdf/pdf/10.330/appl/10](https://www.mdpi.com/journals/pdf/pdf/10.330/appl/10)). detect features at an ultra-spatial resolution of a few centimeters [3]. As such, segmentation of these UAV images has become the basis for pothole characterization and improving segmentation accuracy is of great interest to remote sensing scientists. When imaged using UAV systems, image segmentation becomes the basis for characterizing potholes. Although there are several remote sensing image segmentation methods capable of detecting and characterizing potholes such as spatial-based image segmentation [4], hybrid-technology-based image segmentation [5] and semantic-based image segmentation [6], spectral-based image segmentation has been, and is still, of great interest to the remote sensing community [7]. This method segments an image by analyzing the pixel values [8], through either feature space clustering [9] or thresholding [10]. Of these spectral-based approaches, thresholding is recognized as the most viable and practical way for extracting information from UAV imagery [11], and potholes are no exception. The thresholding approach performs under the supposition that the distribution pixel intensity in the imagery has two different peaks, and the threshold value can be determined by any intensity in the valley that separates them [12]. The threshold is defined as a probability point which confines the criteria for allowing certain processes on the image [13]. As such, the statistical perception known as \"entropy\" is often used for the image thresholding [14]. This approach, conventionally popular for image binarization, segments image into categories of pixels with values that are either lower, greater than or equal to the threshold [15]. The inclusion of the thresholding approach in machine learning and automated detection of pothole in various studies also highlights the significance of this approach [16]. In a study by [PERSON] and [PERSON] [17], the thresholding technique achieved 88% accuracy in extracting pothole regions. The thresholding approach is efficient when there is a clear distinction between a target feature and the background, such as a pothole and asphalt, exhibiting a sharp contrast in their spectral reflectance. However, the thresholding approach has been commonly deployed on grayscale images [18]. As these images are the product of converted multispectral imagery, they contain less details about potholes [14]. Because multispectral imagery contains additional information such as hue and saturation [19], this emphasizes a need to shift focus towards multispectral image thresholding [20]. Image thresholding is not without disadvantages. It relies on information at pixel level, disregarding pixel association with neighboring pixels [21]. Exacerbating the situation are radiometric errors associated with ultra-spatial resolution UAV images, which enable them to detect even smaller features [22], resulting in undesirable noise. These radiometric errors emanate because of the local image contrast, tonal range, random noise and radiometric resolution [23].