Browse > Article
http://dx.doi.org/10.7780/kjrs.2022.38.4.1

Cloud Removal Using Gaussian Process Regression for Optical Image Reconstruction  

Park, Soyeon (Department of Geoinformatic Engineering, Inha University)
Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Publication Information
Korean Journal of Remote Sensing / v.38, no.4, 2022 , pp. 327-341 More about this Journal
Abstract
Cloud removal is often required to construct time-series sets of optical images for environmental monitoring. In regression-based cloud removal, the selection of an appropriate regression model and the impact analysis of the input images significantly affect the prediction performance. This study evaluates the potential of Gaussian process (GP) regression for cloud removal and also analyzes the effects of cloud-free optical images and spectral bands on prediction performance. Unlike other machine learning-based regression models, GP regression provides uncertainty information and automatically optimizes hyperparameters. An experiment using Sentinel-2 multi-spectral images was conducted for cloud removal in the two agricultural regions. The prediction performance of GP regression was compared with that of random forest (RF) regression. Various combinations of input images and multi-spectral bands were considered for quantitative evaluations. The experimental results showed that using multi-temporal images with multi-spectral bands as inputs achieved the best prediction accuracy. Highly correlated adjacent multi-spectral bands and temporally correlated multi-temporal images resulted in an improved prediction accuracy. The prediction performance of GP regression was significantly improved in predicting the near-infrared band compared to that of RF regression. Estimating the distribution function of input data in GP regression could reflect the variations in the considered spectral band with a broader range. In particular, GP regression was superior to RF regression for reproducing structural patterns at both sites in terms of structural similarity. In addition, uncertainty information provided by GP regression showed a reasonable similarity to prediction errors for some sub-areas, indicating that uncertainty estimates may be used to measure the prediction result quality. These findings suggest that GP regression could be beneficial for cloud removal and optical image reconstruction. In addition, the impact analysis results of the input images provide guidelines for selecting optimal images for regression-based cloud removal.
Keywords
Cloud removal; Image reconstruction; Machine learning; Gaussian process;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Belda, S., L. Pipia, P. Morcillo-Pallares, and J. Verrelst, 2020. Optimizing Gaussian process regression for image time series gap-filling and crop monitoring, Agronomy, 10(5): 618. https://doi.org/10.3390/agronomy10050618   DOI
2 Breiman, L., 2001. Random forests, Machine Learning, 45: 5-32. https://doi.org/10.1023/A:1010933404324   DOI
3 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. https://doi.org/10.1016/j.rse.2010.12.010   DOI
4 Kwak, G.-H., C.-W. Park, K.-D. Lee, S.-I. Na, H.-Y. Ahn, and N.-W. Park, 2021. Potential of hybrid CNN-RF model for early crop mapping with limited input data, Remote Sensing, 13(9): 1629. https://doi.org/10.3390/rs13091629   DOI
5 Li, S., L. Xu, Y. Jing, H. Yin, X. Li, and X. Guan, 2021. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques, International Journal of Applied Earth Observation and Geoinformation, 105: 102640. https://doi.org/10.1016/j.jag.2021.102640   DOI
6 Na, S.-I., C.-W. Park, K.-D. So, J.-M. Park, and K.-D. Lee, 2017. Development of garlic & onion yield prediction model on major cultivation regions considering MODIS NDVI and meteorological elements, Korean Journal of Remote Sensing, 33(5-2): 647-659 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2017.33.5.2.5   DOI
7 Park, N.-W., Y. Kim, and G.-H. Kwak, 2019. An overview of theoretical and practical issues in spatial downscaling of coarse resolution satellite-derived products, Korean Journal of Remote Sensing, 35(4): 589-607. https://doi.org/10.7780/kjrs.2019.35.4.8   DOI
8 Ahn, H.-Y., K.-Y. Kim, K.-D. Lee, C.-W. Park, K.-H. So, and S.-I. Na, 2018. Feasibility assessment of spectral band adjustment factor of KOMPSAT-3 for agriculture remote sensing, Korean Journal of Remote Sensing, 34(6-3): 1369-1382 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.6.3.5   DOI
9 Rasmussen, C.E. and C.K.I. Williams, 2006. Gaussian Processes in Machine Learning, The MIT Press, Cambridge, MA, USA.
10 Pipia, L., E. Amin, S. Belda, M. Salinero-Delgado, and J. Verrelst, 2021. Green LAI mapping and cloud gap-filling using Gaussian process regression in Google Earth Engine, Remote Sensing, 13(3): 403. https://doi.org/10.3390/rs13030403   DOI
11 Johnson, M.D., W.W. Hsieh, A.J. Cannon, A. Davidson, and F. Bedard, 2016. Crop yield forecasting on the Canadian Prairies by remotely sensed vegetation indices and machine learning methods, Agricultural and Forest Meteorology, 218: 74-84. https://doi.org/10.1016/j.agrformet.2015.11.003   DOI
12 Verrelst, J., J. Munoz, L. Alonso, J. Delegido, J.P. Rivera, G. Camps-Valls, and J. Moreno, 2012. Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3, Remote Sensing of Environment, 118: 127-139. https://doi.org/10.1016/j.rse.2011.11.002   DOI
13 Camps-Valls, G., J. Verrelst, J. Munoz-Mari, V. Laparra, F. Mateo-Jimenez, and J. Gomez-Dans, 2016. A survey on Gaussian processes for earth-observation data analysis: A comprehensive investigation, IEEE Geoscience and Remote Sensing Magazine, 4(2): 58-78. https://doi.org/10.1109/mgrs.2015.2510084   DOI
14 Kim, Y., G.-H. Kwak, K.-D. Lee, S.-I. Na, C.-W. Park, and N.-W. Park, 2018. Performance evaluation of machine learning and deep learning algorithms in crop classification: Impact of hyper-parameters and training sample size, Korean Journal of Remote Sensing, 34(5): 811-827 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2018.34.5.9   DOI
15 Liu, M., G. Chowdhary, B.C. Da Silva, S.Y. Liu, and J.P. How, 2018. Gaussian processes for learning and control: A tutorial with examples, IEEE Control Systems Magazine, 38(5): 53-86. https://doi.org/10.1109/mcs.2018.2851010   DOI
16 Pasolli, L., F. Melgani, and E. Blanzieri, 2010. Gaussian process regression for estimating chlorophyll concentration in subsurface waters from remote sensing data, IEEE Geoscience and Remote Sensing Letters, 7(3): 464-468. https://doi.org/10.1109/lgrs.2009.2039191   DOI
17 Zhang, C., W. Li, and D. Travis, 2007. Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach, International Journal of Remote Sensing, 28(22): 5103-5122. https://doi.org/10.1080/01431160701250416   DOI
18 Schulz, E., M. Speekenbrink, and A. Krause, 2018. A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions, Journal of Mathematical Psychology, 85: 1-16. https://doi.org/10.1016/j.jmp.2018.03.001   DOI
19 Shen, H. and L. Zhang, 2009. A MAP-based algorithm for destriping and inpainting of remotely sensed images, IEEE Transactions on Geoscience and Remote Sensing, 47(5): 1492-1502. https://doi.org/10.1109/TGRS.2008.2005780   DOI
20 Zeng, C., H. Shen, and L. Zhang, 2013. Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method, Remote Sensing of Environment, 131: 182-194. https://doi.org/10.1016/j.rse.2012.12.012   DOI
21 Verrelst, J., J.P. Rivera, J. Moreno, and G. Camps-Valls, 2013. Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval, ISPRS Journal of Photogrammetry and Remote Sensing, 86: 157-167. https://doi.org/10.1016/j.isprsjprs.2013.09.012   DOI
22 Wang, Q., L. Wang, X. Zhu, Y. Ge, X. Tong, and P.M. Atkinson, 2022. Remote sensing image gap filling based on spatial-spectral random forests, Science of Remote Sensing, 5: 100048. https://doi.org/10.1016/j.srs.2022.100048   DOI
23 Zhu, X., D. Liu, and J. Chen, 2012. A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images, Remote Sensing of Environment, 124: 49-60. https://doi.org/10.1016/j.rse.2012.04.019   DOI
24 Shen, H., X. Li, Q. Cheng, C. Zeng, G. Yang, H. Li, and L. Zhang, 2015. Missing information reconstruction of remote sensing data: A technical review, IEEE Geoscience and Remote Sensing Magazine, 3(3): 61-85. https://doi.org/10.1109/mgrs.2015.2441912   DOI