Abstract
This study evaluates the performance of shadow detection using different image color models. The pixel-level supervised classification procedure employed in this study includes filtering images, creating a trained shadow model, obtaining shadow masks and post-processing of the output masks. Considering the advent of drones usage, we discuss the results of shadow detection on aerial images. Based on the results, the method using YCbCr color features yielded 92.71% average accuracy. The low performance of shadow detection on images with small shadowed regions and images under various weather conditions indicated that additional investigation is necessary to create detection schemes for challenging input images with high spatial resolution.
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