Corrigendum: Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning

In the published article, there were minor inaccuracies, specifically concerning the metrics values for multiple-input models (Table 2) and misclassification values in Table 3. The corrections also extend to the corresponding confusion matrices in the Supplementary Material.

In Table 2, the sub-headers PAcc and UAcc were interchanged. In addition, the metrics for the multiple-input models have been re-evaluated to reflect minor miscalculations in the code. The corrected Table 2 and its caption appear below.

In Table 3, there was a slight miscalculation of some of the statistical values in the columns under “Fraction of original class in the bedrock prediction (%)”, and a transcription error in the value for the original class “Sand, gravel and cobbles” for the model M_B. The corrected Table 3 and its caption appear below.

In 3 Results, paragraph 1, it was stated: “The results for the multiple-input models confirmed the higher predictive power of the depth and slope over backscatter, as all the models incorporating backscatter data (M_BD, M_BH and M_BS) consistently showed lower performance metrics. Noticeably, while M_DS displayed the highest metrics among the multiple-input models, it did not outperform the single-input models M_D and M_S.” The corrected paragraph is as follows:

“The results for the multiple-input models confirmed the higher predictive power of the depth and slope over backscatter, as all the multiple-input models incorporating backscatter data (M_BD, M_BH and M_BS) consistently showed lower performance metrics than the corresponding single-input models without backscatter data (respectively, M_D, M_H, and M_S). Noticeably, no multiple-input models outperformed the best single-input models.”

In 3 Results, paragraph 3, it was stated: “This observation is confirmed by the results listed in Table 2 where the UAcc values for the bedrock class for all the models are higher than the corresponding PAcc ones”. To address the mislabeling of “PAcc” and “UAcc”, the sentence has been corrected as follows:

“This observation is confirmed by the results listed in Table 2 where the PAcc values for the bedrock class for all the models are higher than the corresponding UAcc ones.”

In 3 Results, paragraph 4, it was stated: “While the models generally over-predict the bedrock class, as seen from the higher UAcc values compared to the corresponding PAcc values (Table 2) and from Figure 9, instances of under-prediction are also evident.” To address the mislabeling of “PAcc” and “UAcc”, the sentence has been corrected as follows:

“While the models generally over-predict the bedrock class, as seen from the higher PAcc values compared to the corresponding UAcc values (Table 2) and from Figure 9, instances of under-prediction are also evident.”

In 3 Results, paragraph 5, it was stated: ‘As an example of the table interpretation, for the original class “exposed bedrock” and for the model , the 19.92% of the totality of pixels predicted as bedrock, corresponds to the original class “exposed bedrock”.’ Modifying a percentage value as per Table 3, the sentence has been corrected as follows:

‘As an example of the table interpretation, for the original class “exposed bedrock” and for the model , 19.95% of the totality of pixels predicted as bedrock, corresponds to the original class “exposed bedrock”.’

In 4 Discussion, paragraph 3, it was stated: “M_BD, M_BH and M_BS showed comparable performance to one another but a lower performance compared to the single-layer depth models (Table 2).” The sentence has been corrected as follows:

“M_BD, M_BH and M_BS showed a varied range of performance, but it was in each case lower compared to the corresponding single-layer model without the backscatter layer (Table 2).”

To reflect the updated metric values for the multiple-input models, Supplementary Figures S5–S10 have been updated.

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Statements

Summary

Keywords

deep-learning, seabed, segmentation, multibeam, backscatter, bathymetry, classification

Citation

Garone RV, Birkenes Lønmo TI, Gregory Schimel AC, Diesing M, Thorsnes T and Løvstakken L (2024) Corrigendum: Seabed classification of multibeam echosounder data into bedrock/non-bedrock using deep learning. Front. Earth Sci. 12:1415557. doi: 10.3389/feart.2024.1415557

Received

10 April 2024

Accepted

23 April 2024

Published

16 May 2024

Volume

12 - 2024

Edited and reviewed by

Davide Oppo, University of Louisiana at Lafayette, United States

Updates

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Copyright

© 2024 Garone, Birkenes Lønmo, Gregory Schimel, Diesing, Thorsnes and Løvstakken.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rosa Virginia Garone, rosa.v.garone@ntnu.no

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