Iterative Thresholding-Based Shadow Detection Approach for UAV Images

Shadow detection is a critical task in computer vision and image processing that aims to identify shadow regions in an image. Accurate detection of the shadow is essential for various applications, such as object recognition, scene understanding, and image segmentation. The detection of shadow is difficult due to their complex and dynamic nature, as they can vary in shape, size, and intensity depending on the location of the illumination source, weather conditions, and the characteristics of the scene. In this study, a new shadow detection method has been proposed that automatically calculates the threshold value using an iterative thresholding scheme and detects shadow. The performance of the developed method is tested on four publicly available UAV image datasets related to two study areas namely urban and mining areas. The comparison of the proposed method with several state-of-the-art methods demonstrates that the proposed method performs well in both qualitative and quantitative evaluations, with good overall accuracy in all images.