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Remote Sensing
Volume 14
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10.3390/rs14122841
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Correction to Remote Sens. 2020, 12(23), 3888.
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Correction

Correction: Peng et al. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888

by
Mingyuan Peng
1,2,
Lifu Zhang
1,*,
Xuejian Sun
1,
Yi Cen
1 and
Xiaoyang Zhao
1,2
1
The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(12), 2841; https://doi.org/10.3390/rs14122841
Submission received: 16 July 2021 / Accepted: 22 October 2021 / Published: 14 June 2022
Versions Notes

Error in Affiliation

In the original article [1], there was an error regarding the affiliation 1. The word ‘Research’ was missing. The correct is as follows:
The State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.

Error in Table

In the original article, there was a mistake in Table 5 as published in [1]. The ESTARFM and FSDAF results of CIA and LGC were rightly recorded yet wrongly calculated. The corrected Table 5 appears below.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.

Reference

  1. Peng, M.; Zhang, L.; Sun, X.; Cen, Y.; Zhao, X. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888. [Google Scholar] [CrossRef]
Table
Table 5. Running times 1 of the entire time series using different methods.
Table 5. Running times 1 of the entire time series using different methods.
CIALGCRDT
STF3DCNN55298777
ESTARFM65,871.94109,361.79514,435.744
FSDAF27,626.01751,858.2987595.211
DCSTFN691012,278489.4740
1 Times are expressed in seconds.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Peng, M.; Zhang, L.; Sun, X.; Cen, Y.; Zhao, X. Correction: Peng et al. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888. Remote Sens. 2022, 14, 2841. https://doi.org/10.3390/rs14122841

AMA Style

Peng M, Zhang L, Sun X, Cen Y, Zhao X. Correction: Peng et al. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888. Remote Sensing. 2022; 14(12):2841. https://doi.org/10.3390/rs14122841

Chicago/Turabian Style

Peng, Mingyuan, Lifu Zhang, Xuejian Sun, Yi Cen, and Xiaoyang Zhao. 2022. "Correction: Peng et al. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888" Remote Sensing 14, no. 12: 2841. https://doi.org/10.3390/rs14122841

APA Style

Peng, M., Zhang, L., Sun, X., Cen, Y., & Zhao, X. (2022). Correction: Peng et al. A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset. Remote Sens. 2020, 12, 3888. Remote Sensing, 14(12), 2841. https://doi.org/10.3390/rs14122841

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