Post-Processing for Shadow Detection in Drone-Acquired Images Using U-NET

Siti-Aisyah Zali, Shahbe Mat-Desa, Zarina Che-Embi and Wan-Noorshahida Mohd-Isa
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Siti-Aisyah Zali: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, Cyberjaya 63100, Malaysia
Shahbe Mat-Desa: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, Cyberjaya 63100, Malaysia
Zarina Che-Embi: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, Cyberjaya 63100, Malaysia
Wan-Noorshahida Mohd-Isa: Faculty of Computing and Informatics, Multimedia University, Jalan Multimedia, Cyberjaya 63100, Malaysia

Future Internet, 2022, vol. 14, issue 8, 1-18

Abstract: Shadows in drone images commonly appear in various shapes, sizes, and brightness levels, as the images capture a wide view of scenery under many conditions, such as varied flying height and weather. This property of drone images leads to a major problem when it comes to detecting shadow and causes the presence of noise in the predicted shadow mask. The purpose of this study is to improve shadow detection results by implementing post-processing methods related to automatic thresholding and binary mask refinement. The aim is to discuss how the selected automatic thresholding and two methods of binary mask refinement perform to increase the efficiency and accuracy of shadow detection. The selected automatic thresholding method is Otsu’s thresholding, and methods for binary mask refinement are morphological operation and dense CRF. The study shows that the proposed methods achieve an acceptable accuracy of 96.43%.

Keywords: shadow detection; deep learning; U-Net; aerial images; automatic thresholding; binary mask refinement; post-processing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
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