Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985
1
Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
2
Department of Regional and Urban Planning, College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(4), 783; https://doi.org/10.3390/rs13040783
Submission received: 9 February 2021
/
Accepted: 16 February 2021
/
Published: 20 February 2021
The authors wish to make the following correction to this paper [1]:
Error in Figure/Table
1. In the original article, there was a mistake in Figure 9. as published. We found a spelling and color marking error in the original Figure 9B, the colors of the red curve and grey curve in the original figure should be exchanged, and ‘weightede’ should be replaced with ‘weighted’. The corrected ** Figure 9.** appears below. We wish to replace
with
Figure 9.
Progression of loss values (A) and training accuracy (B) for four loss functions used with Nested SE-Deeplab during training. The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce).
Figure 9.
Progression of loss values (A) and training accuracy (B) for four loss functions used with Nested SE-Deeplab during training. The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce).
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.
2. In the original article, there was a mistake in Table 3. as published. We are aware that some IoU values in the Table 3 were missed to be updated, so we wish to make this correction. The corrected ** Table 3.** appears below. We wish to replace
with
Table 3.
Quantitative comparison of three backbone networks for the testing dataset.
| Experiment | Methods | Correctness | F1-Score | IoU 1 |
|---|---|---|---|---|
| Figure 10a | ResNet | 0.9100 | 0.9112 | 0.8385 |
| ResNext | 0.9088 | 0.9161 | 0.8455 | |
| SE-Net | 0.9140 | 0.9167 | 0.8462 | |
| Figure 10b | ResNet | 0.8152 | 0.8274 | 0.7056 |
| ResNext | 0.8395 | 0.8447 | 0.7312 | |
| SE-Net | 0.8380 | 0.8541 | 0.7454 | |
| Figure 10c | ResNet | 0.7941 | 0.8095 | 0.6800 |
| ResNext | 0.8120 | 0.8190 | 0.6935 | |
| SE-Net | 0.8270 | 0.8254 | 0.7027 |
1 The full name of IoU is Intersection over Union.
The authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original article has been updated.
Conflicts of Interest
The authors declare no conflict of interest.
Reference
- Lin, Y.; Xu, D.; Wang, N.; Shi, Z.; Chen, Q. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. [Google Scholar] [CrossRef]
Figure 9.
Progression of loss values (A) and training accuracy (B) for four loss functions used with Nested SE-Deeplab during training. The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce).
Figure 9.
Progression of loss values (A) and training accuracy (B) for four loss functions used with Nested SE-Deeplab during training. The loss functions are softmax cross entropy (softmax), weighted log loss, dice coefficient (dice), and dice coefficient added with binary cross entropy (bce).
Table 3.
Quantitative comparison of three backbone networks for the testing dataset.
| Experiment | Methods | Correctness | F1-Score | IoU 1 |
|---|---|---|---|---|
| Figure 10a | ResNet | 0.9100 | 0.9112 | 0.8385 |
| ResNext | 0.9088 | 0.9161 | 0.8468 | |
| SE-Net | 0.9140 | 0.9167 | 0.8462 | |
| Figure 10b | ResNet | 0.8152 | 0.8274 | 0.7179 |
| ResNext | 0.8395 | 0.8447 | 0.7312 | |
| SE-Net | 0.8380 | 0.8541 | 0.7454 | |
| Figure 10c | ResNet | 0.7941 | 0.8095 | 0.6957 |
| ResNext | 0.8120 | 0.8190 | 0.7194 | |
| SE-Net | 0.8270 | 0.8254 | 0.7243 |
1 The full name of IoU is Intersection over Union.
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MDPI and ACS Style
Lin, Y.; Xu, D.; Wang, N.; Shi, Z.; Chen, Q. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sens. 2021, 13, 783. https://doi.org/10.3390/rs13040783
AMA Style
Lin Y, Xu D, Wang N, Shi Z, Chen Q. Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985. Remote Sensing. 2021; 13(4):783. https://doi.org/10.3390/rs13040783
Chicago/Turabian StyleLin, Yeneng, Dongyun Xu, Nan Wang, Zhou Shi, and Qiuxiao Chen. 2021. "Correction: Lin, Y., et al. Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens. 2020, 12, 2985" Remote Sensing 13, no. 4: 783. https://doi.org/10.3390/rs13040783
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