Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bist B (2018) Literature survey on unmanned aerial vehicle. Int J Pure Appl Math 119(12):4381–4387
Yang W, Guo W, Peng K, Liu L (2012) Research on removing shadow in workpiece image based on homomorphic filtering. Procedia Eng 29:2360–2364
Sharma D, Singhai J (2019) An object-based shadow detection method for building delineation in high-resolution satellite images. PFG-J Photogram Remote Sens Geoinf Sci 87:103–118
Arévalo V, González J, Ambrosio G (2008) Shadow detection in colour high-resolution satellite images. Int J Remote Sens 29(7):1945–1963
Tian J, Qi X, Qu L, Tang Y (2016) New spectrum ratio properties and features for shadow detection. Pattern Recogn 51:85–96
Polidorio AM, Flores FC, Imai NN, Tommaselli AM, Franco C (2003) Automatic shadow segmentation in aerial color images. In: 16th Brazilian symposium on computer graphics and image processing. IEEE, pp 270–277
Huang J, Xie W, Tang L (2004) Detection of and compensation for shadows in colored urban aerial images. In: Fifth world congress on intelligent control and automation, vol 4. IEEE, pp 3098–3100
Tsai VJ (2006) A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Trans Geosci Remote Sens 44(6):1661–1671
Anoopa S, Dhanya V, Kizhakkethottam JJ (2016) Shadow detection and removal using tri-class based thresholding and shadow matting technique. Procedia Technol 24:1358–1365
Das RK, Shandilya M (2019) Optimization of shadow detector and color model index using automatic threshold and boundary refinement. Int J Eng Tech 5:1303–2395
Alvarado-Robles G, Osornio-Rios RA, Solis-Munoz FJ, Morales-Hernandez LA (2021) An approach for shadow detection in aerial images based on multi-channel statistics. IEEE Access 9:34240–34250
Pons X, Padró JC (2019) An empirical approach on shadow reduction of UAV imagery in forests. In: IEEE international geoscience and remote sensing symposium. IEEE, pp 2463–2466
Wang Q, Yan L, Yuan Q, Ma Z (2017) An automatic shadow detection method for VHR remote sensing orthoimagery. Remote Sens 9(5):469
Usha Nandini D, Leni ES (2019) Efficient shadow detection by using PSO segmentation and region-based boundary detection technique. J Supercomput 75:3522–3533
Silva GF, Carneiro GB, Doth R, Amaral LA, de Azevedo DF (2018) Near real-time shadow detection and removal in aerial motion imagery application. ISPRS J Photogram Remote Sens 140:104–121
Kang X, Huang Y, Li S, Lin H, Benediktsson JA (2017) Extended random walker for shadow detection in very high resolution remote sensing images. IEEE Trans Geosci Remote Sens 56(2):867–876
Luo S, Li H, Zhu R, Gong Y, Shen H (2021) ESPFNet: an edge-aware spatial pyramid fusion network for salient shadow detection in aerial remote sensing images. IEEE J Sel Top Appl Earth Observ Remote Sens 14:4633–4646
Zali SA, Mat-Desa S, Che-Embi Z, Mohd-Isa WN (2022) Post-processing for shadow detection in drone-acquired images using u-net. Future Internet 14(8):231
Zhang H, Sun K, Li W (2014) Object-oriented shadow detection and removal from urban high-resolution remote sensing images. IEEE Trans Geosci Remote Sens 52(11):6972–6982
Drone mapping software, image processing and geospatial, dronemapper (2022). https://dronemapper.com/. 14 Mar 2023
Garcia L, Diaz J, Correa HL, Restrepo-Girón A (2020) Thermal and visible aerial imagery. Mendeley Data 2:2020
Institute of Computer Graphics and Vision (2019). http://dronedataset.icg.tugraz.at. 14 Mar 2023
M-Desa S, Zali S, Mohd-Isa WN, Che-Embi Z (2022) Color-based shadow detection method in aerial images. In: J Phys: Conf Ser 2312:012081 (IOP Publishing)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Deeksha, Meenpal, T. (2024). Iterative Thresholding-Based Shadow Detection Approach for UAV Images. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2023. Lecture Notes in Networks and Systems, vol 892. Springer, Singapore. https://doi.org/10.1007/978-981-99-9521-9_28
Download citation
DOI: https://doi.org/10.1007/978-981-99-9521-9_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9520-2
Online ISBN: 978-981-99-9521-9
eBook Packages: EngineeringEngineering (R0)