Browse > Article
http://dx.doi.org/10.7780/kjrs.2021.37.5.1.14

Effect of Correcting Radiometric Inconsistency between Input Images on Spatio-temporal Fusion of Multi-sensor High-resolution Satellite Images  

Park, Soyeon (Department of Geoinformatic Engineering, Inha University)
Na, Sang-il (National Institute of Agricultural Sciences, Rural Development Administration)
Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.5_1, 2021 , pp. 999-1011 More about this Journal
Abstract
In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutionsfrom multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used asinput. In particular, the improvement in prediction performance wassubstantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.
Keywords
Spatio-temporal fusion; multi-sensor images; radiometric correction; spectral response;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Joyce, K.E., S.E. Belliss, S.V. Samsonov, S.J. McNeill, and P.J. Glassey, 2009. A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters, Progress in Physical Geography, 33(2): 183-207.   DOI
2 Kim, Y. and N.-W. Park, 2019. Comparison of spatiotemporal fusion models of multiple satellite images for vegetation monitoring, Korean Journal of Remote Sensing, 35(6-3): 1209-1219 (in Korean with English abstract).   DOI
3 Leach, N., N.C. Coops, and N. Obrknezev, 2019. Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies, Computers and Electronics in Agriculture, 164: 104893.   DOI
4 Sadeh, Y., X. Zhu, D. Dunkerley, J.P. Walker, Y. Zhang, O. Rozenstein, and K. Chenu, 2021. Fusion of Sentinel-2 and PlanetScope time-series data into daily 3 m surface reflectance and wheat LAI monitoring, International Journal of Applied Earth Observation and Geoinformation, 96: 102260.   DOI
5 Wilson, N., J. Greenberg, A. Jumpasut, A. Collison, and H. Weichelt, 2017. Absolute Radiometric Calibration of Planet Dove Satellites, Flocks 2p and 2e, https://earth.esa.int/eogateway/documents/20142/1305226/Absolute-Radiometric-Calibration-Planet-DoveFlocks-2p-2e.pdf, Accessed on Aug. 30, 2021.
6 Zhang, W., A. Li, H. Jin, J. Bian, Z. Zhang, G. Lei, and C. Huang, 2013. An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data, Remote Sensing, 5(10): 5346-5368.   DOI
7 Zhao, Y., B. Huang, and H. Song, 2018. A robust adaptive spatial and temporal image fusion model for complex land surface changes, Remote Sensing of Environment, 208: 42-62.   DOI
8 Zhu, X., F. Cai, J. Tian, and T.K.A. Williams, 2018. Spatiotemporal fusion of multisource remote sensing data: Literature survey, taxonomy, principles, applications, and future directions, Remote Sensing, 10(4): 527.   DOI
9 Park, S., Y. Kim, S.-I. Na, and N.-W. Park, 2020. Evaluation of spatio-temporal fusion models of multi-sensor high-resolution satellite Images for crop monitoring: An experiment on the fusion of Sentinel-2 and RapidEye images, Korean Journal of Remote Sensing, 36(5-1): 807-821 (in Korean with English abstract).   DOI
10 Kim, Y., P.C. Kyriakidis, and N.-W. Park, 2020. A cross-resolution, spatiotemporal geostatistical fusion model for combining satellite image time-series of different spatial and temporal resolutions, Remote Sensing, 12(10): 1553.   DOI
11 Canty, M.J., A.A. Nielsen, and M. Schmidt, 2004. Automatic radiometric normalization of multitemporal satellite imagery, Remote Sensing of Environment, 91(3-4): 441-451.   DOI
12 Dennison, P.E., K.Q. Halligan, and D.A. Roberts, 2004. A comparison of error metrics and constraints for multiple endmember spectral mixture analysis and spectral angle mapper, Remote Sensing of Environment, 93(3): 359-367.   DOI
13 Belgiu, M. and A. Stein, 2019. Spatiotemporal image fusion in remote sensing, Remote Sensing, 11(7): 818.   DOI
14 Du, Y., P.M. Teillet, and J. Cihlar, 2002. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection, Remote Sensing of Environment, 82(1): 123-134.   DOI
15 Houborg, R. and M.F. McCabe, 2018. A Cubesat enabled spatio-temporal enhancement method (CESTEM) utilizing Planet, Landsat and MODIS data, Remote Sensing of Environment, 209: 211-226.   DOI
16 Kim, J., S. Kang, B. Seo, A. Narantsetseg, and Y. Han, 2020. Estimating fractional green vegetation cover of Mongolian grasslands using digital camera images and MODIS satellite vegetation indices, GIScience and Remote Sensing, 57(1): 49-59.   DOI
17 Gabr, B., M. Ahmed, and Y. Marmoush, 2020. PlanetScope and Landsat 8 imageries for bathymetry mapping, Journal of Marine Science and Engineering, 8(2): 143.   DOI
18 Gao, F., J. Masek, M. Schwaller, and F. Hall, 2006. On the blending of the Landsat and MODIS surface reflectance: Predicting daily Landsat surface reflectance, IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218.   DOI
19 Gevaert, C.M. and F.J. Garcia-Haro, 2015. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion, Remote Sensing of Environment, 156: 34-44.   DOI
20 Giri, C.P., 2012. Remote Sensing of Land Use and Land Cover: Principles and Applications, CRC Press, Boca Raton, FL, USA.
21 Hong, G. and Y. Zhang, 2008. A comparative study on radiometric normalization using high resolution satellite images, International Journal of Remote Sensing, 29(2): 425-438.   DOI
22 Jang, E., Y.J. Kim, J. Im, and Y.-G. Park, 2021. Improvement of SMAP sea surface salinity in river-dominated oceans using machine learning approaches, GIScience and Remote Sensing, 58(1): 138-160.   DOI
23 Latte, N. and P. Lejeune, 2020. PlanetScope radiometric normalization and Sentinel-2 super-resolution (2.5 m): A straightforward spectral-spatial fusion of multi-satellite multi-sensor images using residual convolutional neural networks, Remote Sensing, 12(15): 2366.   DOI
24 Yang, X. and C.P. Lo, 2000. Relative radiometric normalization performance for change detection from multi-date satellite images, Photogrammetric Engineering and Remote Sensing, 66(8): 967-980.
25 Zhou, J., J. Chen, X. Chen, X. Zhu, Y. Qiu, H. Song, and X. Cui, 2021. Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction, Remote Sensing of Environment, 252: 112130.   DOI
26 Planet, 2021. Planet Imagery Product Specifications, https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf, Accessed on Aug. 31, 2021.
27 Zhu, Z., 2017. Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications, ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384.   DOI