Cloud Recognition in Hyperspectral Satellite Images Using an Explainable Machine Learning Model

Z. Li, H. Shen, H. Li, G. Xid, P. Gamba, and L. Zhang, “Multi-feature combined cloud and cloud-shadow detection in Gaofen-1 widefield of view imagery,” Remote Sens. Environ. 191, 342–358 (2017). https://doi.org/10.1016/j.rse.2017.01.026

Article  ADS  Google Scholar 

P. Bo, S. Fenzhen, and M. Yunshan, “A cloud and cloud shadow detection methods based on Fuzzy C-Means algorithm,” IEEE J. Sel. Top. Appl. Earth. Obs. Remote Sens. 13, 1714–1727 (2020). https://doi.org/10.1109/JSTARS.2020.2987844

Article  ADS  Google Scholar 

L. Sun, X. Mi, J. Wei, J. Wang, X. Tian, H. Yu, and P. Gan, “A cloud detection algorithm generating method for remote sensing data at visible to short-wave infrared wavelengths,” ISPRS J. Photogramm. 125 (D24), 70–88 (2017). https://doi.org/10.1016/j.isprsjprs.2016.12.005

Article  Google Scholar 

L. Sun, J. Wei, J. Wang, X. Mi, Y. Guo, Y. Lv, Y. Yang, P. Gan, X. Zhou, C. Jio, C. Jiawei, and X. Tian, “A Universal Dynamic Threshold Cloud Detection Algorithm (UNSADA) supported by a prior surface,” J. Geophys. Res.: Atmos. 121 (12), 7172–7196 (2016). https://doi.org/10.1002/2015JD024722

Article  ADS  Google Scholar 

G. Mateo-Garcia, L. Gomez-Chova, J. Amoros-Lopez, J. Munoz-Mari, and G. Camps-Valls, “Multitemporal cloud masking in the Google Earth Engine,” Remote Sens. 10 (7), 1079 (2018). https://doi.org/10.3390/rs10071079

Article  ADS  Google Scholar 

A. Lyapustin, Y. Wang, and R. Frey, “An automatic cloud mask algorithm based on time series of MODIS measurements,” J. Geophys. Res. 113, D16207 (2008). https://doi.org/10.1029/2007JD009641

Article  ADS  Google Scholar 

J. Bian, A. Li, Q. Liu, and C. Huang, “Cloud and snow discrimination for CCD images of HJ-1A/B constellation based on spectral signature and spatio-temporal context,” Remote Sens. 8 (31) (2016). https://doi.org/10.3390/rs8010031

A. M. Belov and A. Yu. Denisova, “Scene distortion detection algorithm using multitemporal remote sensing images”, Comp. Opt. 43 (5), 869–885 (2019). https://doi.org/10.18287/2412-6179-2019-43-5-869-885

Article  Google Scholar 

O. Hagolle, M. Huo, Pascual D. Villa, and G. Dedieu, “A multi-temporal method for cloud detection, applied to Formosat-2, VeNμS, Landsat, and Sentinel-2 images,” Remote Sens. Environ. 114 (8), 1747–1755 (2010). https://doi.org/10.1016/j.rse.2010.03.002

Article  ADS  Google Scholar 

X. Zhu and E. H. Helmer, “An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy region,” RSE 214, 135–153 (2018). https://doi.org/10.1016/j.rse.2018.05.024

Article  ADS  Google Scholar 

Yu. V. Vizilter, V. S. Gorbatsevich, and S. Yu. Zheltov, “Structure-functional analysis and synthesis of deep convolutional neural networks,” Comp. Opt. 43 (5), 886–900 (2019). https://doi.org/10.18287/2412-6179-2019-43-5-886-900

Article  Google Scholar 

Y. Shendryk, Y. Rist, C. Ticehurst, and P. Thorburn, “Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery,” ISPRS J. Photogramm. 157, 124–136 (2019). https://doi.org/10.1016/j.isprsjprs.2019.08.018

Article  Google Scholar 

A. I. Andreev and Yu. A. Shamilova, “Cloud detection using Himawari-8 satellite with a convolutional neural network,” Issled. Zemli Kosmosa, No. 2, 42–52 (2021). https://doi.org/10.31857/S0205961421010036

Article  Google Scholar 

M. Zheng, W. Tang, and X. Zhao, “Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing,” Int. J. Geogr. Inf. Sci. 33, 314–345 (2019). https://doi.org/10.1080/13658816.2018.1530355

Article  Google Scholar 

H. Fu, Y. Shen, J. Liu, G. He, J. Chen, P. Liu, J. Qian, and J. Li, “Cloud detection for FY meteorology satellite based on ensemble thresholds and random forests approach,” Remote Sens. 11 (1), 44 (2019). https://doi.org/10.3390/rs11010044

Article  ADS  Google Scholar 

N. Ghasemian and M. Akhoondzadeh, “Integration of VIR and thermal bands for cloud, snow/ice and thin cirrus detection in MODIS satellite images,” in Proc. of the Third International Conference on Intelligent Decision Science, Tehran, Iran, May 1–37, 2018 (Tehran, 2018), pp. 1–37.

H. Liu, D. Zeng, and Q. Tian, “Super-pixel cloud detection using hierarchical fusion CNN,” in Proc. of the Fourth International Conference on Multimedia Big Data (IEEE, 2018), pp. 1–6. https://doi.org/10.1109/BigMM.2018.8499091

L. Wang, Y. Chen, L. Tang, R. Fan, and Y. Yao, “Object-based convolutional neural networks for cloud and snow detection in high-resolution multispectral imagers,” Water 10 (11), 1666 (2018). https://doi.org/10.3390/w10111666

Article  Google Scholar 

L. Gilpin, D. Bau, B. Yuan, A. Bajwa, M. Specter, and L. Kagal, “Explaining explanations: An overview of interpretability of machine learning,” in The 5th International Conference on Data Science and Advanced Analytics (DSAA) Turin, Italy, 2018 (IEEE, 2018), pp. 80–89. https://doi.org/10.1109/DSAA.2018.00018

E. Strumbelj and I. Kononenko, “Explaining prediction models and individual predictions with feature contributions,” Knowl. Inf. Syst. 41, 647–665 (2014).

Article  Google Scholar 

N. R. Goodwin, L. J. Collet, R. J. Denham, N. Flood, and D. Tindall, “Cloud and cloud shadow screening across Queensland, Australia: An automated method for LandsatTM/ETA + time-series,” Remote Sens. Environ. 134, 50–65 (2013). https://doi.org/10.1016/j.rse.2013.02.019

Article  ADS  Google Scholar 

P. Mishra, Python AI Model Explainability (DMK-Press, Moscow, 2022) [in Russian].

Google Scholar 

T. Hastie, R. Tibshirani, and J. Friedman, “Additive models, trees, and related methods,” in The Elements of Statistical Learning (Springer, 2009), pp. 295–336.

Book  Google Scholar 

F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), USA, 2017 (IEEE, 2017), pp. 1800–1807. https://doi.org/10.1109/CVPR.2017.195

留言 (0)

沒有登入
gif