Correction: Niu et al. SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer. Remote Sens. 2022, 15, 949
1
Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China
2
Naval Research Institute, Beijing 100070, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(12), 2994; https://doi.org/10.3390/rs15122994
Submission received: 17 May 2023
/
Accepted: 19 May 2023
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Published: 8 June 2023
(This article belongs to the Special Issue Recent Progress of Change Detection Based on Remote Sensing)
1. Missing Citation
In the original publication [1], a reference about dataset was not cited. The citation has now been inserted in Section 4.1, Paragraph 2 and should read:
The Landsat-SCD dataset [39] is made up of Landsat images collected between 1990 and 2020.
The following reference will be added in the section “References” as Reference 39:
Yuan, P.; Zhao, Q.; Zhao, X.; Wang, X.; Long, X.; Zheng, Y. A transformer-based Siamese network and an open-optical dataset for semantic-change detection of remote sensing images. Int. J. Digit. Earth 2022, 15, 1506–1525.
2. Text Correction
There were some flaws in the original publication. In Abstract and Conclusions, the experimental results only mentioned the excellent performance on the SECOND dataset and did not talk about the performance on the Landsat-SCD dataset. So we would like to add to it.
A correction has been made to Abstract:
Extensive experimental results on the challenging SECOND and Landsat-SCD datasets, demonstrate that our SMNet obtains 71.95% and 85.65% at mean Intersection over Union (mIoU), respectively. In addition, the proposed SMNet achieves 20.29% and 51.14% at Separated Kappa coefficient (Sek) on the SECOND and Landsat-SCD datasets, respectively. All of the above proves the effectiveness and superiority of the proposed method.
A correction has been made to Conclusions, Paragraph 1:
Comparative experimental results on the SECOND and Landsat-SCD datasets show that our new framework shows better accuracy.
A correction has been made to Conclusions, Paragraph 2:
The SECOND and Landsat-SCD datasets are currently available datasets for remote sensing image SCD tasks with pixel-level annotations of large-scale images.
3. The Order of the References
Due to adding new References, the numeration of References with respect to the original publication has been modified. References [39,40,41] in the original paper will appear in the document as References [40,41,42], respectively. With this correction, the order of some references has been adjusted accordingly.
The authors state that the scientific conclusions are unaffected. The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.
Reference
- Niu, Y.; Guo, H.; Lu, J.; Ding, L.; Yu, D. SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer. Remote Sens. 2023, 15, 949. [Google Scholar] [CrossRef]
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MDPI and ACS Style
Niu, Y.; Guo, H.; Lu, J.; Ding, L.; Yu, D. Correction: Niu et al. SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer. Remote Sens. 2022, 15, 949. Remote Sens. 2023, 15, 2994. https://doi.org/10.3390/rs15122994
AMA Style
Niu Y, Guo H, Lu J, Ding L, Yu D. Correction: Niu et al. SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer. Remote Sens. 2022, 15, 949. Remote Sensing. 2023; 15(12):2994. https://doi.org/10.3390/rs15122994
Chicago/Turabian StyleNiu, Yiting, Haitao Guo, Jun Lu, Lei Ding, and Donghang Yu. 2023. "Correction: Niu et al. SMNet: Symmetric Multi-Task Network for Semantic Change Detection in Remote Sensing Images Based on CNN and Transformer. Remote Sens. 2022, 15, 949" Remote Sensing 15, no. 12: 2994. https://doi.org/10.3390/rs15122994
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