Abstract
Monitoring water is a complex task due to its dynamic nature, added pollutants, and land build-up. The availability of high-resolution data by Sentinel-2 multispectral products makes implementing remote sensing applications feasible. However, overutilizing or underutilizing multispectral bands of the product can lead to inferior performance. In this work, we compare the performances of ten out of the thirteen bands available in a Sentinel-2 product for water segmentation using eight machine learning algorithms. We find that the shortwave-infrared bands (B11 and B12) are the most superior for segmenting water bodies. B11 achieves an overall accuracy of \(71\%\) while B12 achieves \(69\%\) across all algorithms on the test site. We also find that the Support Vector Machine (SVM) algorithm is the most favorable for single-band water segmentation. The SVM achieves an overall accuracy of \(69\%\) across the tested bands over the given test site. Finally, to demonstrate the effectiveness of choosing the right amount of data, we use only B11 reflectance data to train an artificial neural network, BandNet. Even with a basic architecture, BandNet is proportionate to known architectures for semantic and water segmentation, achieving a 92.47 mIOU on the test site. BandNet requires only a fraction of the time and resources to train and run inference, making it suitable to be deployed on web applications to run and monitor water bodies in localized regions. Our codebase is available at https://github.com/IamShubhamGupto/BandNet.
Work done partially while interning at CDSAML, PES University and RRSC-S, ISRO.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324
Chan, Golub, L.: Updating formulae and a pairwise algorithm for computing sample variances. http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf (1979)
Chen, J., Li, Y., Ma, Q., Shen, X., Zhao, A., Li, J.: Preliminary evaluation of sentinel-2 bottom of atmosphere reflectance using the 6sv code in Beijing area. In: IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium. pp. 7760–7763 (2018). https://doi.org/10.1109/IGARSS.2018.8517598
Chen, L.C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation (2017)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation (2018)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 785–794. KDD ’16, ACM, New York, NY, USA (2016). https://doi.org/10.1145/2939672.2939785. http://doi.acm.org/10.1145/2939672.2939785
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1800–1807 (2017)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Cox, D.R.: The regression analysis of binary sequences. J. Roy. Stat. Soc. Ser. B (Methodol) 20(2), 215–232 (1958)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL visual object classes challenge 2012 (VOC2012) results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/inde-x.html
Isikdogan, L., Bovik, A., Passalacqua, P.: Seeing through the clouds with deepwatermap. IEEE Geosci. Remote Sens. Lett. PP, 1–5 (2019). https://doi.org/10.1109/LGRS.2019.2953261
Islam, M., Rochan, M., Bruce, N., Wang, Y.: Gated feedback refinement network for dense image labeling. pp. 4877–4885 (07 2017). https://doi.org/10.1109/CVPR.2017.518
Kazama, S., Oki, T.: The effects of climate change on water resources (2006)
Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., et al.: Swin Transformer V2: Scaling Up Capacity and Resolution. arXiv preprint arXiv:2111.09883 (2021)
Luo, X., Tong, X., Hu, Z.: An applicable and automatic method for earth surface water mapping based on multispectral images. Int. J. Appl. Earth Obs. Geoinf. 103, 102472 (2021)
Picard, D.: Torch. manual_seed (3407) is all you need: On the influence of random seeds in deep learning architectures for computer vision. arXiv preprint arXiv:2109.08203 (2021)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
Ruder, S.: An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. pp. 4510–4520 (2018). 10.1109/CVPR.2018.00474
Wei, Y., Hu, H., Xie, Z., Zhang, Z., Cao, Y., Bao, J., Chen, D., Guo, B.: Contrastive learning rivals masked image modeling in fine-tuning via feature distillation. arXiv preprint arXiv:2205.14141 (2022)
Yang, L., Driscol, J., Sarigai, S., Wu, Q., Lippitt, C.D., Morgan, M.: Towards synoptic water monitoring systems: a review of ai methods for automating water body detection and water quality monitoring using remote sensing. Sensors 22(6) (2022). https://doi.org/10.3390/s22062416, https://www.mdpi.com/1424-8220/22/6/2416
Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., Shum, H.Y.: Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605 (2022)
Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classification. In: Hu, X., Liu, Q., Skowron, A., Lin, T.Y., Yager, R.R., Zhang, B. (eds.) GrC. pp. 718–721. IEEE (2005). http://cs.nju.edu.cn/zhouzh/zhouzh.files/publication/grc05.pdf
Acknowledgements
This work has been supported by Center of Data Science and Applied Machine Learning, Computer Science and Engineering Department of PES University, and Regional Remote Sensing Centre—south. We would like to thank Dr. Shylaja, S. S. of PES University and Dr. K. Ganesha Raj of Regional Remote Sensing Centre - south for the opportunity to carry out this work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gupta, S., Uma, D., Hebbar, R. (2023). Analysis and Application of Multispectral Data for Water Segmentation Using Machine Learning. In: Tistarelli, M., Dubey, S.R., Singh, S.K., Jiang, X. (eds) Computer Vision and Machine Intelligence. Lecture Notes in Networks and Systems, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-19-7867-8_56
Download citation
DOI: https://doi.org/10.1007/978-981-19-7867-8_56
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7866-1
Online ISBN: 978-981-19-7867-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)