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http://dx.doi.org/10.7848/ksgpc.2019.37.3.167

Evaluation of Rededge-M Camera for Water Color Observation after Image Preprocessing  

Kim, Wonkook (Dept. Civil and Environmental Engineering, Pusan National University)
Roh, Sang-Hyun (REDONE TECHNOLOGIES Co. Ltd.)
Moon, Yongseon (Dept. Electric Engineering, Sunchon National University)
Jung, Sunghun (Department of Electric Vehicle Engineering, Dongshin University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.37, no.3, 2019 , pp. 167-175 More about this Journal
Abstract
Water color analysis allows non-destructive estimation of abundance of optically active water constituents in the water body. Recently, there have been increasing needs for light-weighted multispectral cameras that can be integrated with low altitude unmanned platforms such as drones, autonomous vehicles, and heli-kites, for the water color analysis by spectroradiometers. This study performs the preprocessing of the Micasense Rededge-M camera which recently receives a growing attention from the earth observation community for its handiness and applicability for local environment monitoring, and investigates the applicability of Rededge-M data for water color analysis. The Vignette correction and the band alignment were conducted for the radiometric image data from Rededge-M, and the sky, water, and solar radiation essential for the water color analysis, and the resultant remote sensing reflectance were validated with an independent hyperspectral instrument, TriOS RAMSES. The experiment shows that Rededge-M generally satisfies the basic performance criteria for water color analysis, although noticeable differences are observed in the blue (475 nm) and the near-infrared (840 nm) band compared with RAMSES.
Keywords
Water Color; Remote Sensing; Micasense Rededge-M; Band Alignment; Preprocessing; Multispectral Camera;
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1 Goldman, D.B. (2010), Vignette and exposure calibration and compensation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 12, pp. 2276-2288.   DOI
2 Jhan, J., Rau, J., and Huang, C. (2016), Band-to-band registration and ortho-rectification of multilens/multispectral imagery: A case study of MiniMCA-12 acquired by a fixed-wing UAS, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 114, pp. 66-77.   DOI
3 Kim, H. (2014), Utilization Plan of Drones for the Field of Ocean and Fishery, Issue Analysis Report No. 2014-06, Korea Maritime Institute (KMI), Busan, pp. 5-36. (in Korean)
4 Kim, S.J., and Pollefeys, M. (2008), Robust radiometric calibration and vignetting correction, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 4, pp. 562-576.   DOI
5 Le, C.F., Hu, C.M, Cannizzaro, J., English, D., Muller-Karger, F., and Lee, Z. (2013), Evaluation of chlorophyll-a remote sensing algorithms for an optically complex estuary, Remote Sensing of Environment, Vol. 129, pp. 75-89.   DOI
6 Lee, Z., Ahn, Y.H., Mobley, C., and Arnone, R. (2010), Removal of surface-reflected light for the measurement of remote-sensing reflectance from an above-surface platform. Optics Express, Vol. 18, No. 25, pp. 26313-26324.   DOI
7 Lee, Z., Carder, K.L., Mobley, C.D., Steward, R.G., and Patch, J.S. (1999), Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Applied Optics, Vol. 38, No. 18, pp. 3831-3843.   DOI
8 Neville, R.A., and Gower, J.F.R. (1977), Passive remote sensing of phytoplankton via chlorophyll ${\alpha}$ fluorescence, Journal of Geophysical Research, Vol. 82, No. 24, pp. 3487-3493.   DOI
9 O'Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A., Carder, K.L., Garver, S.A., Kahru, M., and McClain, C. (1998), Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research-Oceans, Vol. 103, No. C11, pp. 24,937-24,953.   DOI
10 Shang, S., Lee, Z., Lin, G., Hu, C., Shi, L., Zhang, Y., Li, X., Wu, J., and Yan, J. (2017), Sensing an intense phytoplankton bloom in the western Taiwan Strait from radiometric measurements on a UAV, Remote Sensing of Environment, Vol. 198, pp. 85-94.   DOI
11 Becker, R.H., Sayers, M., Dehm, D., Shuchman, R., Quintero, K., Bosse, K., and Sawtell, R. (2019), Unmanned aerial system based spectroradiometer for monitoring harmful algal blooms: A new paradigm in water quality monitoring, Journal of Great Lakes Research, Vol. 45, No. 3, pp. 444-453.   DOI
12 Zheng, Y., Lin, S., Kambhamettu, C., Yu, J., and Kang, S.B. (2009), Single-image vignetting correction, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 12, pp. 2243-2256.   DOI
13 Zitova, B., and Flusser, J. (2003), Image registration methods: A survey. Image and Vision Computing, Vol. 21, No. 11, pp. 977-1000.   DOI