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http://dx.doi.org/10.7780/kjrs.2021.37.5.1.19

Development of Brightness Correction Method for Mosaicking UAV Images  

Ban, Seunghwan (Program in Smart City Engineering, Inha University)
Kim, Taejung (Department of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_1, 2021 , pp. 1071-1081 More about this Journal
Abstract
Remote Sensing using unmanned aerial vehicles(UAV) can acquire images with higher time resolution and spatial resolution than aerial and satellite remote sensing. However, UAV images are photographed at low altitude and the area covered by one image isrelatively narrow. Therefore multiple images must be processed to monitor large area. Since UAV images are photographed under different exposure conditions, there is difference in brightness values between adjacent images. When images are mosaicked, unnatural seamlines are generated because of the brightness difference. Therefore, in order to generate seamless mosaic image, a radiometric processing for correcting difference in brightness value between images is essential. This paper proposes a relative radiometric calibration and image blending technique. In order to analyze performance of the proposed method, mosaic images of UAV images in agricultural and mountainous areas were generated. As a result, mosaic images with mean brightness difference of 5 and root mean square difference of 7 were avchieved.
Keywords
UAV; Relative Radiometric Calibration; Mosaic; Blending; Alpha Blending; Gain Correction; Agricultural Land;
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  • Reference
1 Brown, M. and D.G. Lowe, 2007. Automatic panoramic image stitching using invariant features, International Journal of Computer Cision, 74(1): 59-73.   DOI
2 Cho, M., J. Kim, and K. Kim, 2018. Three-Dimensional Rotation Angle Preprocessing and Weighted Blending for Fast Panoramic Image Method, Journal of Broadcast Engineering, 23(2): 235-245 (in Korean with English abstract).   DOI
3 Fang, F., T. Wang, Y. Fang, and G. Zhang, 2019. Fast color blending for seamless image stitching, IEEE Geoscience and Remote Sensing Letters, 16(7): 1115-1119.   DOI
4 Kim, H.G., J.I. Kim, S.J. Yoon, and T. Kim, 2018. Development of a method for calculating the allowable storage capacity of rivers by using drone images, Korean Journal of Remote Sensing, 34(2-1): 203-211 (in Korean with English abstract).   DOI
5 Li, M., D. Li, B. Guo, L. Li, T. Wu, and W. Zhang, 2018. Automatic seam-line detection in UAV remote sensing image mosaicking by use of graph cuts, ISPRS International Journal of Geo-Information, 7(9): 361.   DOI
6 Lim, P.C., S. Rhee, J. Seo, J.I. Kim, J. Chi, S.B. Lee, and T. Kim, 2021. An Optimal Image-Selection Algorithm for Large-Scale Stereoscopic Mapping of UAV Images, Remote Sensing, 13(11): 2118.   DOI
7 Nam, D.Y. and J.K. Han, 2019. Color and Illumination Compensation Algorithm for 360 VR Panorama Image, Journal of Broadcast Engineering, 24(1): 3-24 (in Korean with English abstract).   DOI
8 Rhee, S., T. Kim, J. Kim, M.C. Kim, and H.J. Chang, 2015. DSM generation and accuracy analysis from UAV images on river-side facilities, Korean Journal of Remote Sensing, 31(2): 183-191 (in Korean with English abstract).   DOI
9 Shin, J.I., Y.M. Cho, P.C. Lim, H.M. Lee, H.Y. Ahn, C.W. Park, and T. Kim, 2020. Relative Radiometric Calibration Using Tie Points and Optimal Path Selection for UAV Images, Remote Sensing, 12(11): 1726.   DOI
10 Tian, Q.C. and L.D. Cohen, 2017. Histogram-based color transfer for image stitching, Journal of Imaging, 3(3): 38.   DOI
11 Chen, K. and M. Wang, 2014. Image stitching algorithm research based on OpenCV, Proc. of the 33rd Chinese Control Conference, Nanjing, CN, Jul. 28-30, vol. 11, pp. 7292-7297.   DOI