Semi-Supervised Learning for Sediment Classification Using Convolutional Neural Networks with Digital Elevation Model and Backscatter Data

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Abstract

Accurate sediment classification is crucial for advancing marine research, environmental monitoring, and sustainable seabed use. However, acquiring large amounts of labeled data in such settings is often challenging, expensive, and time-consuming. To address this limitation, a semi-supervised learning framework has been proposed that leverages convolutional neural networks for sediment classification using both labeled and unlabeled data. The approach utilizes pseudo-labeling, where confident predictions on unlabeled samples are iteratively incorporated into training to enhance model generalization. The method is applied and evaluated on a dataset that includes multi-modal inputs such as a digital elevation model and multibeam sonar backscatter data. Experimental results indicate that semi-supervised learning with convolutional neural networks can achieve high classification accuracy in scenarios characterized by limited labeled data and a large volume of unlabeled data. This approach highlights the potential of deep learning combined with semi-supervised strategies for efficient underwater environment classification.

Keywords:

convolutional neural network (CNN), deep learning, digital elevation model (DEM), multibeam sonar backscatter, pseudo label, residual neural network (ResNet), sediment classification, semi-supervised learning (SSL)

References


  1. Chen M., Du Y., Zhang Y., Qian S., Wang C. (2024), Semi-supervised learning with multi-head co-training, arXiv, http://arxiv.org/abs/2107.04795

  2. Dosovitskiy A. et al. (2021), An image is worth 16x16 words: Transformers for image recognition at scale, arXiv, http://arxiv.org/abs/2010.11929

  3. Fonseca L., Calder B. (2005), Geocoder: An efficient backscatter map constructor, [in:] Proceedings of the U.S. Hydro 2005 Conference.

  4. Hady M.F.A., Schwenker F. (2013), Semi-supervised learning, [in:] Handbook on Neural Information Processing, Bianchini M., Maggini M., Jain L. [Eds], 49: 215–239, Springer, Berlin, Heidelberg, https://doi.org/10.1007/978-3-642-36657-4˙7

  5. He K., Zhang X., Ren S., Sun J. (2016), Deep residual learning for image recognition, [in:] 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, https://doi.org/10.1109/CVPR.2016.90

  6. Kingma D.P., Ba J. (2017), Adam: A method for stochastic optimization, arXiv, http://arxiv.org/abs/1412.6980

  7. Krizhevsky A., Sutskever I., Hinton G.E. (2012), ImageNet classification with deep convolutional neural networks, [in:] Proceedings of the 26th International Conference on Neural Information Processing Systems.

  8. Lee D.H. (2013), Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, [in:] Workshop on Challenges in Representation Learning.

  9. Lemaitre G., Nogueira F., Aridas C.K. (2017), Imbalanced-learn: A Python toolbox to tackle the curse of imbalanced datasets in machine learning, Journal of Machine Learning Research, 18(17): 1–5.

  10. Parnum I.M., Gavrilov A.N. (2011), High-frequency multibeam echo-sounder measurements of seafloor backscatter in shallow water: Part 2 – Mosaic production, analysis and classification, Underwater Technology, 30(1): 13–26, https://doi.org/10.3723/ut.30.013

  11. Paszke A. et al. (2019), PyTorch: An imperative style, high-performance deep learning library, [in:] Proceedings of the 33rd International Conference on Neural Information Processing Systems.

  12. Qin X., Luo X., Wu Z., Shang J. (2021), Optimizing the Sediment Classification of Small Side-Scan Sonar Images Based on Deep Learning, IEEE Access, 9: 29416–29428, https://doi.org/10.1109/ACCESS.2021.3052206

  13. Schimel A.C.G. et al. (2018), Multibeam sonar backscatter data processing, Marine Geophysical Research, 39: 121–137, https://doi.org/10.1007/s11001-018-9341-z

  14. Song Z., Yang X., Xu Z., King I. (2021), Graph-based semi-supervised learning: A comprehensive review, arXiv, http://arxiv.org/abs/2102.13303

  15. Tan M., Le Q. (2019), EfficientNet: Rethinking model scaling for convolutional neural networks, [in:] Proceedings of the 36th International Conference on Machine Learning, 97: 6105–6114.

  16. Zhao Y., Zhu K., Zhao T., Zheng L., Deng X. (2023), Small-sample seabed sediment classification based on deep learning, Remote Sensing, 15(8): 2178, https://doi.org/10.3390/rs15082178