Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method
Sadhana R. Sonvane1 , U.B. Solapurkar2
Section:Research Paper, Product Type: Journal Paper
Volume-7 ,
Issue-6 , Page no. 1065-1075, Jun-2019
CrossRef-DOI: https://doi.org/10.26438/ijcse/v7i6.10651075
Online published on Jun 30, 2019
Copyright © Sadhana R. Sonvane, U.B. Solapurkar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Sadhana R. Sonvane, U.B. Solapurkar, “Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.1065-1075, 2019.
MLA Style Citation: Sadhana R. Sonvane, U.B. Solapurkar "Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method." International Journal of Computer Sciences and Engineering 7.6 (2019): 1065-1075.
APA Style Citation: Sadhana R. Sonvane, U.B. Solapurkar, (2019). Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method. International Journal of Computer Sciences and Engineering, 7(6), 1065-1075.
BibTex Style Citation:
@article{Sonvane_2019,
author = {Sadhana R. Sonvane, U.B. Solapurkar},
title = {Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {1065-1075},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4682},
doi = {https://doi.org/10.26438/ijcse/v7i6.10651075}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.10651075}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4682
TI - Object Shadow Detection and Removal from Remote Sensing Images using Successive Thresholding Method
T2 - International Journal of Computer Sciences and Engineering
AU - Sadhana R. Sonvane, U.B. Solapurkar
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 1065-1075
IS - 6
VL - 7
SN - 2347-2693
ER -
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Abstract
Now a day’s capturing a live images with high quality plays an crucial role in all the fields. It is more important as far as security in military, commercial, household fields as well as to monitor the continuous changes in earth surfaces are concern. Most of the time to achieve clear images we have to differentiate between original object and shadow as detecting objects under the influence of shadow is a challenging task. In urban area the shadow produces artificial color features and shape deformation of objects which decays the quality of image. Shadow mainly occurs due to elevate objects and If light source has been blocked by some obstacles. However, a lot of shadowed areas in remote sensing images of urban areas have affected the tasks, such as image classification, object detection and recognition. Tsai presented an efficient algorithm which uses the ratio value of the hue over the intensity to construct the ratio map for detecting shadows of color aerial images. Instead of only using the global thresholding process in Tsai’s algorithm, this paper presents a novel successive thresholding scheme (STS) to detect shadows more accurately. By performing the global thresholding process on the modified ratio map, a coarse-shadow map is constructed to classify the input color aerial image into the shadow pixels and the non-shadow pixels. Instead of only using the global thresholding process in Tsai’s algorithm, this paper presents a novel successive thresholding scheme (STS) to detect shadows more accurately. For the three four testing images, which contain some low brightness objects, our proposed algorithm has better shadow detection accuracy when compared with the previous shadow detection algorithms proposed by Tsai. Thus for the correct image interpretation it is important to detect shadow regions and restore their information. So it is very essential to detect the shadow regions and remove it effectively to get useful information with good quality.
Key-Words / Index Term
Shadow detection method, Successive Thresholding Algorithm, Shadow removal, Otsu’s method, Image Segmentation, Tsai’s algorithm, Adaptive Histogram Equalization and Image Adjustment
References
[1] Hongya zhang, Kaimin sun, and Wenzhuo li,” Object-oriented Shadow Detection and Removal from Urban High-resolution Remote Sensing Images”, IEEE transactions on Geoscience and Remote Sensing, vol. 52, no. 11, november 2014.
[2] Kuo-Liang Chung, Yi-Ru Lin, and Yong-Huai Huang,”Efficient Shadow Detection of Color Aerial Images Based on Successive Thresholding Scheme”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 2, February 2009.
[3] Aliaksei Makarau, Rudolf Richter, Rupert Müller and Peter Reinartz,” Adaptive Shadow Detection Using a Blackbody Radiator Model”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 49, No. 6, June 2011.
[4] P.S.Ramesh, S. Letitia, “A Novel Approach For Shadows Detection And Shadows Removal From High Resolution Satellite Images,” African Journal of Basic & Applied Sciences 9(4):243-250, 2017.
[5] Dong Cai , Manchun Li , Zhiliang Bao,”Study on Shadow Detection Method on High Resolution Remote Sensing Image Based on HIS Space Transformation and NDVI Index”, 18th International Conference on Geoinformatics, 18-20 June 2010.
[6] P. Sarabandi ,F. Yamazaki , M. Matsuoka,”Shadow Detection and Radiometric Restoration in Satellite High Resolution Images”. IEEE International Geoscience and Remote Sensing Symposium, 20-24 Sept. 2004
[7] Luca Lorenzi, Farid Melgani, and Grégoire Mercier,” A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 9, september 2012.
[8] Ling Zhang , Qing Zhang , Chunxia Xiao,” Shadow Remover: Image shadow removal based on illumination recovering optimization”. IEEE Transaction on Image Processing. Volume: 24 , Issue: 11 Year : 2015.
[9] Danang Surya Candra, Stuart Phinn, Peter Scarth,”Cloud and Cloud Shadow Removal Of Landsat 8 Images Using Multitemporal Cloud Removal Method”, 6th International Conference on Agro Geoinformatics 7-10 Aug. 2017.
[10] Shuang Luo , Huifang Li , Huanfeng Shen,”Shadow Removal Based on Clustering Correction of Illumination Field for Urban Aerial Remote Sensing Images”, IEEE International Conference on Image Processing (ICIP) Year: 2017.
[11] Vertika Jain , Ajay Khunteta,“Shadow Removal for Umbrageous Information Recovery in Aerial Images”. International Conference on Computer, Communications and Electronics (Comptelix) Year: 2017.
[12] Geethu Vijayan , S. R. Reshma , F. E. Dhanya ; S. Anju , Gayathri R. Nair ; R. P. Aneesh,” A Novel Shadow Removal Algorithm using Niblack Segmetation in Satellite Images,” International Conference on Communication Systems and Networks (ComNet) Year: 2016.
[13] Bin Pan ; Junfeng Wu ; Zhiguo Jiang ; Xiaoyan Luo,” Shadow Detection in remote Sensing Images based on Weighted Edge Gradient Ratio”, IEEE Geoscience and Remote Sensing Symposium Year: 2014.
[14] Nan Su ; Ye Zhang ; Shu Tian ; Yiming Yan ; Xinyuan Miao,” Shadow Detection and Removal for Occluded Object Information Recovery in Urban High-Resolution Panchromatic Satellite Images”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Year: 2016 , Volume: 9 , Issue: 6.
[15] Hongmei Zhu , Jihao Yin , Ding Yuan , Xiang Liu , Guangyun Zhang,”Dem-based shadow detection and removal forlunar craters”. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2016.
[16] Lei Ma , Bitao Jiang , Xinwei Jiang , Ye Tian,” Shadow removal in remote sensing images using features sample matting”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Year: 2015.
[17] W. Zhou, G. Huang, A. Tr oy, and M. L. Cadenasso, “Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study,” Remote Sens. E nv., vol. 113, no. 8, pp. 1769–1777, 2009.
[18] Victor J. D. Tsai,” A Comparative Study on Shadow Compensation of Color Aerial Images in Invariant Color Models”, IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 6, june 2006.
[19] Rafael C. Gonzalez, Richard E.Woods ,”Digital Image Processing”, Dorling Kindersley publisher, India.