Skip to main content
SpringerLink
Log in

Deep residual U-Net for automatic detection of Moroccan coastal upwelling using SST images

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Upwelling phenomenon is one of the most important dynamic process in the ocean, which brings nutrients from the depths of the ocean into the surface layer, leading to an enhancement of the primary production and playing a considerable role in the coastal ecosystem. Deep learning (DL) based segmentation methods have been providing state-of-the-art performance in the last few years. These methods have been successfully applied to oceanic remote sensing image segmentation, classification, and detection tasks. In particular, U-Net, has become one of the most popular for these applications. This paper proposes UpwellRes-Net, a deep fully convolutional neural network architecture, for automatic upwelling detection and pixel-segmentation on sea surface temperature (SST) images. The proposed model is based on U-Net structure and residual learning, thus, combining the strengths of both approaches. The main objective of this study is to investigate the performance of deep learning in the extraction of upwelling area. Hence, UpwellRes-Net is trained and optimized on satellite-derived SST database provided by the Moderate Resolution Imaging Spectroradiometer (MODIS). Experiments on the southern Atlantic Moroccan coast show the superiority of the proposed model to a transfer learning based model developed for the same. Deep learning based upwelling detection system can be a cost effective, accurate and convenient way for objective analysis of upwelling phenomenon.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Atillah A et al (2005) Produits opérationnels d’océanographie spatiale pour le suivi et l’analyse du phénomene d’upwelling marocain. Geo Observateur 14:49–62

    Google Scholar 

  2. Bezdek C, Pal SK (1994) Fuzzy models for pattern recognition, USDOE Pittsburgh Energy Technology Center, PA (United States); Oregon State, Tech Rep

  3. Canziani A, Paszke A, Culurciello E (2016) An analysis of deep neural network models for practical applications. arXiv, arXiv:1605.07678.

  4. Chaudhari S, Balasubramanian R, Gangopadhyay A (2008) Upwelling detection in AVHRR sea surface temperature (SST) images using neural-network framework. In: IGARSS 2008-2008 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp IV-926–IV-929. https://doi.org/10.1109/IGARSS.2008.4779875

  5. Defence Science and Technology Laboratory (n.d.) Dstl satellite imagery feature detection. [Online]. Available: https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/. Accessed 06 Feb 2019

  6. Drozdzal M, Vorontsov E, Chartrand G, Kadoury S, Pal C (2016) The importance of skip connections in biomedical image segmentation. CoRR, abs/1608.04117

  7. Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7)

  8. Goodfellow I, Bengio Y, Courville A (2016) Deep learning (adaptive computation and machine learning

  9. Hassaballah M, Awad AI (2020) Deep learning in computer vision: principles and applications. CRC Press. https://doi.org/10.1201/9781351003827

  10. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034

  11. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  12. Iglovikov V, Shvets A (2018) Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv preprint arXiv:1801.05746

  13. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning (ICML), Lile, France, pp. 448–456

  14. Kingma DP, Ba JA (2015) A method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (ICLR), San Diego, CA, USA, p. 13

  15. Lguensat R, Sun M, Fablet R et al (2018) EddyNet: A deep neural network for pixel-wise classification of oceanic eddies. In: IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, pp 1764–1767. https://doi.org/10.1109/IGARSS.2018.8518411

  16. Li R, Liu W, Yang L, Sun S, Hu W, Zhang F, Li W (2018) Deepunet: a deep fully convolutional network for pixel-level sea-land segmentation. IEEE J Sel Top Appl Earth Obs Remote Sens 11(11):3954–3962. https://doi.org/10.1109/JSTARS.2018.2833382

  17. Li X, Liu B, Zheng G, Ren Y, Zhang S, Liu Y, Gao L, Liu Y, Zhang B, Wang F (2020) Deep-learning-based information mining from ocean remote-sensing imagery. Natl Sci Rev 7(10):1584–1605. https://doi.org/10.1093/nsr/nwaa047

    Article  Google Scholar 

  18. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL (2014) Microsoft coco: common objects in context. In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision – ECCV 2014. Springer International Publishing, Cham, pp 740–755

    Chapter  Google Scholar 

  19. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440. https://doi.org/10.1109/CVPR.2015.7298965

  20. Lu X, Ma C, Ni B, Yang X, Reid I, Yang MH (2018) Deep regression tracking with shrinkage loss, eccv

  21. Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: Unsupervised video object segmentation with co-attention siamese networks, cvpr

  22. Lu X, Wang W, Shen J, Crandall D, Luo J (2020) Zero-Shot Video Object Segmentation with Co-Attention Siamese Networks. IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/TPAMI.2020.3040258

  23. Lu X, Ma C, Shen J, Yang X, Reid I, Yang MH (2020) Deep object tracking with shrinkage loss. IEEE transactions on pattern analysis and machine intelligence. https://doi.org/10.1109/TPAMI.2020.3041332

  24. Mann KH, Lazier JRN (2013) Dynamics of marine ecosystems: biological-physical interactions in the oceans. John Wiley & Sons. https://doi.org/10.1007/BF00042919

  25. Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE, pp 565–571. https://doi.org/10.1109/3DV.2016.79

  26. Nascimento S, Franco P, Sousa F, Dias J, Neves F (2012) Automated computational delimitation of SST upwelling areas using fuzzy clustering. Comput Geosci 43:207–216. https://doi.org/10.1016/j.cageo.2011.10.025

  27. Nieto K, Demarcq H, McClatchie S (2005) Mesoscale frontal structures in the canary upwelling system: new front and filament detection algorithms applied to spatial and temporal patterns. Remote Sens Environ 123:339–346

    Article  Google Scholar 

  28. Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9:62–66

    Article  Google Scholar 

  29. Rapantzikos K, Zervakis M, Balas K (2003) Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med Image Anal 7(1):95–98

    Article  Google Scholar 

  30. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, pp. 234–241

  31. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Li F-F (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis (IJCV) 115(3):211–252

    Article  Google Scholar 

  32. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556

  33. Szegedy C, Liu W, Jia YQ, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  34. Tamim A, Minaoui K, Daoudi K, Yahia H, Atillah A, Smiej MF, Aboutajdine D (2013) A simple and efficient approach for coarse segmentation of Moroccan coastal upwelling. In: Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European, pages 1–5

  35. Tamim A, Minaoui K, Daoudi K, Yahia H, Atillah A, Aboutajdine D (2015) An efficient tool for automatic delimitation of Moroccan coastal upwelling using SST images. IEEE Geosci Remote Sens Lett 12(4):875–879. https://doi.org/10.1109/LGRS.2014.2365558

  36. Tamim A, Minaoui K, Daoudi K, Yahia H, Atillah A, el Fellah S, Aboutajdine D, el Ansari M (2019) Automatic detection of Moroccan coastal upwelling zones using sea surface temperature images. Int J Remote Sens 40(7):2648–2666

    Article  Google Scholar 

  37. Vargas CA, González HE (2004) Plankton community structure and carbon cycling in a coastal upwelling system. I. Bacteria, microprotozoans and phytoplankton in the diet of copepods and appendicularians. Aquat Microb Ecol 34(2):151–164

    Article  Google Scholar 

  38. Yuan Q et al (2020) Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ 241:111716

    Article  Google Scholar 

  39. Zhu XX, Tuia D, Mou L, Xia G-S, Zhang L, Xu F, Fraundorfer F (2017) Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sens Mag 5(4):8–36. https://doi.org/10.1109/MGRS.2017.2762307

Download references

Author information

Authors and Affiliations

  1. Faculty of Science, LABSIV Computer Science, Ibn Zohr University, Agadir, Morocco

    Mohamed Snoussi, Ayoub Tamim, Salma El Fellah & Mohamed El Ansari

  2. Department of Marine Fisheries, Higher Institute of Marine Fisheries (ISPM), Agadir, Morocco

    Ayoub Tamim

  3. LRIT, Associated Unit to CNRST (URAC No 29), Rabat IT Center-Faculty of Sciences, Mohammed V University, Rabat, Morocco

    Ayoub Tamim & Salma El Fellah

  4. Informatics and Applications Laboratory Department of Computer Science Faculty of Science, My Ismail University, Meknès, Morocco

    Mohamed El Ansari

Authors
  1. Mohamed Snoussi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ayoub Tamim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Salma El Fellah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed El Ansari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Snoussi.

Ethics declarations

Conflict of interest

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed.

We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property.

We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Snoussi, M., Tamim, A., El Fellah, S. et al. Deep residual U-Net for automatic detection of Moroccan coastal upwelling using SST images. Multimed Tools Appl 82, 7491–7507 (2023). https://doi.org/10.1007/s11042-022-13692-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13692-4

Keywords

Search

Navigation