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http://dx.doi.org/10.7780/kjrs.2020.36.5.4.8

Evaluation of the Utility of SSG Algorithm for Image Restoration of Landsat-8  

Lee, Mi Hee (Division of Disaster Information Research, National Disaster Management Research Institute)
Lee, Dalgeun (Division of Disaster Information Research, National Disaster Management Research Institute)
Yu, Jung Hum (Division of Disaster Information Research, National Disaster Management Research Institute)
Kim, Jinyoung (Division of Disaster Information Research, National Disaster Management Research Institute)
Publication Information
Korean Journal of Remote Sensing / v.36, no.5_4, 2020 , pp. 1231-1244 More about this Journal
Abstract
Landsat satellites are representative optical satellites that have observed the Earth's surface for a long-term, and are suitable for long-term changes such as disaster preparedness/recovery monitoring, land use change, change detection, and time series monitoring. In this paper, clouds and cloud shadows were detected using QA bands to detect and remove clouds simply and efficiently. Then, the missing area of the experimantal image is restorated through the SSG algorithm, which does not directly refer to the pixel value of the reference image, but performs restoration to the pixel value in the Experimental image. Through this study, we presented the possibility of utilizing the modified SSG algorithm by quantitatively and qualitatively evaluating information on variousl and cover conditions in the thermal wavelength band as well as the visible wavelength band observing the surface.
Keywords
Landsat-8; cloud detection; image Restortion; disaster monitoring;
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1 Sun, L., X. Mi, J. Wei, J. Wang, X. Tian, H. Yu, and P. Gan, 2017. A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths, ISPRS Journal of Photogrammetry and Remote Sensing, 124: 70-88.   DOI
2 Terixeira Pinto, C., X. Jing, and L. Leigh, 2020. Evaluation analysis of Landsat level-1 and level-2 data products using in situ measurements, Remote Sensing, 12(16): 2597.   DOI
3 Tulbure, M. G., M. Broich, S. V. Stehman, and A. Kommareddy, 2016. Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region, Remote Sensing of Environment, 178: 142-157.   DOI
4 USGS(Department of the Interior U.S. Geological Survey), 2019. LANDSAT 8 surface reflectance code (LASRC) product guide, LSDS-1368, Version 2.0, EROS Sioux Falls, South Dakota
5 Wang, B., A. Ono, K. Muramatsu, and N. Fujiwara, 1999. Automated detection and removal of clouds and their shadows from Ladnsat TM images, IEICE Transactions on Information and Systems, 82(2): 453-460.
6 Willmott, C. J. and K. Matsuura, 2005. Advantages of the mean absolute error(MAE) over the root mean equare error(RMSE) in assessing average model performance, Climate Research, 30(1): 79-82.   DOI
7 Zhang, C., W. Li, and D. Travis, 2007. Gaps-Fill of SLC-off Landsat ETM Plus satellite image using a geostatistical approach, International Journal of Remote Sensing, 28(22): 5103-5122.   DOI
8 Zhu, Z. and C. E. Woodcock, 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: An algorithm designed specifically for monitoring land cover change, Remote Sensing of Environment, 152: 217-234.   DOI
9 Zhu, Z., 2017, Change detection using Landsat time serires : A review of frequencies, preprocessing, algorithms, and applications, ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384.   DOI
10 Zhu, Z., S. Qiu, B. He, and C. Deng, 2018. Cloud and cloud shadow detection for Landsat images: The fundamental basis for analyzing Landsat time series, Remote Sensing Time Series Image Processing, CRC Peress, 25-46.
11 Zhu, X., F. Gao, D. Liu, and J. Chen, 2012. A modified neighborhood similar pixel interpolators approach for removing thick clouds in Landsat images, IEEE Geoscience and Remote Sensing, 9(3): 521-525.   DOI
12 Zi, Y., F. Xie, and Z. Jiang, 2018. A cloud detection method for Landsat 8 images based on PCANet, Remote Sensing, 10(6): 877.   DOI
13 Hansen, M. C. and T. R. Loveland, 2012. A review of large area monitoring of land cover change using Landsat data, Remote Sensing of Environment, 122: 66-74.   DOI
14 Bullock, E. L., C. E. Woodcock, and P. Olofsson, 2020. Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis, Remote Sensing of Environment, 238: 110968.   DOI
15 Chen, J., X. Zhu, J. E. Vogelmann, F. Gao, and S. Jin, 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-Off images, Remote Sensing of Environment, 115(4): 1053-1064.   DOI
16 Durnev, A., R. Morck, B. Yeung, and P. Zarowin, 2003. Does greather firm-specific return variation mean more or less informed stock pricing?, Journal of Accounting Reseach, 41(5): 797-836.   DOI
17 Helmer, E. H. and B. Ruefenacht, 2005. Cloud-Free satellite image mosaics with regression trees and histogram matching, Photogrammetric Engineering and Remote Sensing, 71(9): 1079-1089.   DOI
18 Jin, S., C. Homer, L. Yang, G. Xian, J. Fry, P. Danielson, and P. A. Townsend, 2013. Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA, International Journal of Remote Sensing, 34(5): 1540-1560.   DOI
19 Kim, B. H., Y. H. Kim, Y. K. Han, W. S. Choi, and Y. I. Kim, 2014. Fully automated generation of cloud-free imagery using Landsat-8, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 32(2): 133-142 (in Korean with English abstract).   DOI
20 Kim, H, O., J. M. Yeom, and Y. S. Kim, 2011. The multi-temporal characteristics of spectral vegetation indices for agricultural land use on RapidEye satellite imagery, Aerospace Engineering and Technology, 10(1): 149-155.
21 Roy, D. P., 2000. The Impact of misregistration upon composited wide field of view satellite data and implications for change detection, IEEE Transactions on Geoscience and Remote Sensing, 38: 2017-2032.   DOI
22 Lee, H. W. and K. S. Lee, 2015. Development of cloud detection method with geostationary ocean color imagery for land applications, Korean Journal of Remote Sensing, 31(5): 371-384.   DOI
23 Margono, B. A., S. Turubanova, I. Zhuravleva, P. Potapov, A. Tyukavina, A. Baccini, S. Goetz, and M. C. Hansen, 2012. Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010, Environmental Research Letters, 7(3): 034010.   DOI
24 Morck, R., B. Yeung, and W. Yu, 2000. The information content of stock markets: why do emerging markets have synchronous stock price movements?, Journal of Financial Economics, 58(1-2): 215-260.   DOI
25 Roy, D. P., J. Ju, P. Lewis, C. Schaaf, F. Gao, M. Hansen, and E. Lindquist, 2008. Multi-Temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data, Remote Sensing of Environment, 112(6): 3112-3130.   DOI
26 Rossow, W. B. and L. C. Garder, 1993. cloud detection using satellite measurements of infrared and visible radiances for ISCCP, Journal of Climate, 6(12): 2341-2369.   DOI
27 Saunders, R. W. and K. T. Kriebel, 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data, International Journal of Remote Sensing, 9(1): 123-150.   DOI