Skip to main content
SpringerLink
Log in

Pixel Level Feature Extraction and Machine Learning Classification for Water Body Extraction

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Surface Water Bodies (SWB) are a renewable water source crucial for maintaining ecosystems and the water cycle. The declining rate of SWB increases owing to the overutilization of these resources, especially for agriculture. A timely and accurate Surface Water Body Extraction (SWBE) is necessary for water resource conservation and planning. Recently, Deep Learning (DL), a subset of Machine Learning (ML) algorithm, got remarkable attention in SWBE. It learns inherent features directly from the images at the expense of time and data. But, the ML algorithms such as K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGB) use optimal hand-crafted features to produce better results with fewer data and time. In this paper, SWBE is performed through two steps: (1) Use of spectral indices and Gabor filters for obtaining Pixel Level Feature (PLF) maps from the multispectral image; (2) Prediction of water and non-water pixels based on PLF maps using the KNN, DT, RF, SVM, and XGB classifiers. The proposed framework has experimented with Resoucesat-2 imagery over major reservoirs in Tamil Nadu and India. The results show that the proposed PLF + XGB outperforms in accuracy, recall, F1-score, kappa, False Negative Rate, Mathews Correlation Coefficient, and mean Intersection over Union with the metric value of 0.995, 0.990, 0.983, 0.979, 0.009, 0.979, and 0.969 with other existing and proposed models. Also, the surface water extent of Bhavani Sagar and Sathanur reservoirs is predicted for 4 years (2016–2019) and the causes of surface water dynamics were analyzed.

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
Fig. 7
Fig. 8
Fig.9
Fig. 10

Similar content being viewed by others

References

  1. Niemczynowicz, J.: Urban hydrology and water management—present and future challenges. Urban Water 1(1), 1–14 (1999). https://doi.org/10.1016/s1462-0758(99)00009-6

    Article  Google Scholar 

  2. Fatahi Nafchi, R.; Yaghoobi, P.; Reaisi Vanani, H.; Ostad-Ali-Askari, K.; Nouri, J.; Maghsoudlou, B.: Eco-hydrologic stability zonation of dams and power plants using the combined models of SMCE and CEQUALW2. Appl. Water Sci. (2021). https://doi.org/10.1007/s13201-021-01427-z

    Article  Google Scholar 

  3. Fletcher, T.; Andrieu, H.; Hamel, P.: Understanding, management and modelling of urban hydrology and its consequences for receiving waters: a state of the art. Adv. Water Resour. 51, 261–279 (2013). https://doi.org/10.1016/j.advwatres.2012.09.001

    Article  Google Scholar 

  4. Zhou, Y.; Luo, J.; Shen, Z.; Hu, X.; Yang, H.: Multiscale water body extraction in urban environments from satellite images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(10), 4301–4312 (2014). https://doi.org/10.1109/jstars.2014.2360436

  5. Aboulela, H.; Bantan, R.; Zeineldin, R.: Evaluating and predicting changes occurring on the coastlines of Jeddah City using satellite images. Arab. J. Sci. Eng. 45(1), 327–339 (2019). https://doi.org/10.1007/s13369-019-04085-1

    Article  Google Scholar 

  6. Liu, X.; Huang, Y.; Xin, J.; Wang, P.: Research and development of drought monitoring and information management system in Heilongjiang province. Arab. J. Sci. Eng. 47(1), 667–679 (2021). https://doi.org/10.1007/s13369-021-05762-w

    Article  Google Scholar 

  7. Lira, J.: Segmentation and morphology of open water bodies from multispectral images. Int. J. Remote Sens. 27(18), 4015–4038 (2006). https://doi.org/10.1080/01431160600702384

    Article  Google Scholar 

  8. Sethre, P.; Rundquist, B.; Todhunter, P.: Remote detection of prairie pothole ponds in the Devils Lake Basin, North Dakota. GIS Remote Sens. 42(4), 277–296 (2005). https://doi.org/10.2747/1548-1603.42.4.277

    Article  Google Scholar 

  9. Jain, S.; Singh, R.; Jain, M.; Lohani, A.: Delineation of flood-prone areas using remote sensing techniques. Water Resour. Manag. 19(4), 333–347 (2005). https://doi.org/10.1007/s11269-005-3281-5

    Article  Google Scholar 

  10. Mondejar, J.; Tongco, A.: Near infrared band of Landsat 8 as water index: a case study around Cordova and Lapu-Lapu City, Cebu, Philippines. Sustain. Environ. Res. (2019). https://doi.org/10.1186/s42834-019-0016-5

    Article  Google Scholar 

  11. McFEETERS, S.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17(7), 1425–1432 (1996). https://doi.org/10.1080/01431169608948714

    Article  Google Scholar 

  12. Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27(14), 3025–3033 (2006). https://doi.org/10.1080/01431160600589179

    Article  Google Scholar 

  13. Sekertekin, A.: A survey on global thresholding methods for mapping open water body using sentinel-2 satellite imagery and normalized difference water index. Arch. Comput. Methods Eng. 28(3), 1335–1347 (2020). https://doi.org/10.1007/s11831-020-09416-2

    Article  Google Scholar 

  14. Feyisa, G.; Meilby, H.; Fensholt, R.; Proud, S.: Automated Water Extraction Index: a new technique for surface water mapping using Landsat imagery. Remote Sens. Environ. 140, 23–35 (2014). https://doi.org/10.1016/j.rse.2013.08.029

    Article  Google Scholar 

  15. Lacaux, J.; Tourre, Y.; Vignolles, C.; Ndione, J.; Lafaye, M.: Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley Fever epidemics in Senegal. Remote Sens. Environ. 106(1), 66–74 (2007). https://doi.org/10.1016/j.rse.2006.07.012

    Article  Google Scholar 

  16. Mishra, K.; Prasad, P.: Automatic extraction of water bodies from landsat imagery using perceptron model. J. Comput. Environ. Sci. 2015, 1–9 (2015). https://doi.org/10.1155/2015/903465

    Article  Google Scholar 

  17. Lian, L.; Jianfei, C.: Spatial-temporal change analysis of water area in Pearl River delta based on remote sensing technology. Procedia Environ. Sci. 10, 2170–2175 (2011). https://doi.org/10.1016/j.proenv.2011.09.340

    Article  Google Scholar 

  18. Sun, F.; Sun, W.; Chen, J.; Gong, P.: Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery. Int. J. Remote Sens. 33(21), 6854–6875 (2012). https://doi.org/10.1080/01431161.2012.692829

    Article  Google Scholar 

  19. Fu, J.; Wang, J.; Li, J.: Study on the automatic extraction of water body from TM image using decision tree algorithm. In: Proceedings SPIE 6625, International Symposium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications, vol. 662502 (2008). https://doi.org/10.1117/12.790602

  20. Wang, C.; Jia, M.; Chen, N.; Wang, W.: Long-term surface water dynamics analysis based on Landsat imagery and the Google Earth Engine platform: a case study in the middle Yangtze River Basin. Remote Sens. 10(10), 1635 (2018). https://doi.org/10.3390/rs10101635

    Article  Google Scholar 

  21. Sui, Y.; Fu, D.; Wang, X.; Su, F.: Surface water dynamics in the North America Arctic based on 2000–2016 Landsat data. Water 10(7), 824 (2018). https://doi.org/10.3390/w10070824

    Article  Google Scholar 

  22. Klemenjak, S.; Waske, B.; Valero, S.; Chanussot, J.: Automatic detection of rivers in high-resolution SAR data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(5), 1364–1372 (2012). https://doi.org/10.1109/jstars.2012.2189099

    Article  Google Scholar 

  23. Li, J.; Narayanan, R.: A shape-based approach to change detection of lakes using time series remote sensing images. IEEE Trans. Geosci. Remote Sens. 41(11), 2466–2477 (2003). https://doi.org/10.1109/tgrs.2003.817267

    Article  Google Scholar 

  24. Sun, F.; Zhao, Y.; Gong, P.; Ma, R.; Dai, Y.: Monitoring dynamic changes of global land cover types: fluctuations of major lakes in China every 8 days during 2000–2010. Chin. Sci. Bull. 59(2), 171–189 (2013). https://doi.org/10.1007/s11434-013-0045-0

    Article  Google Scholar 

  25. Sun, J.; Wang, G.; He, G.; Pu, D.; Jiang, W.; Li, T.; Niu, X.: Study on the water body extraction using GF-1 data based on adaboost integrated learning algorithm. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLII-3/W10 42, 641–648 (2020). https://doi.org/10.5194/isprs-archives-xlii-3-w10-641-2020

  26. Ghasemigoudarzi, P.; Huang, W.; De Silva, O.; Yan, Q.; Power, D.: A machine learning method for inland water detection using CYGNSS data. IEEE Geosci. Remote Sens. Lett. 19, 1–5 (2022). https://doi.org/10.1109/lgrs.2020.3020223

    Article  Google Scholar 

  27. Huang, X.; Xie, C.; Fang, X.; Zhang, L.: Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(5), 2097–2110 (2015). https://doi.org/10.1109/jstars.2015.2420713

  28. Yu, L.; Wang, Z.; Tian, S.; Ye, F.; Ding, J.; Kong, J.: Convolutional neural networks for water body extraction from Landsat imagery. Int. J. Comput. Intell. Appl. 16(01), 1750001 (2017). https://doi.org/10.1142/s1469026817500018

    Article  Google Scholar 

  29. Chen, Y.; Fan, R.; Yang, X.; Wang, J.; Latif, A.: Extraction of urban water bodies from high-resolution remote-sensing imagery using deep learning. Water 10(5), 585 (2018). https://doi.org/10.3390/w10050585

    Article  Google Scholar 

  30. Krizhevsky, A.; Sutskever, I.; Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386

    Article  Google Scholar 

  31. Simonyan, K.; Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: 3rd International Conference on Learning Representations (ICLR 2015), pp. 1–14 (2015). https://doi.org/10.48550/arXiv.1409.1556

  32. He, K.; Zhang, X.; Ren, S.; Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016). https://doi.org/10.48550/arXiv.1512.03385

  33. Huang, G.; Liu, Z.; Weinberger, K.Q.: Densely connected convolutional networks. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 2261–2269 (2017). https://doi.org/10.1109/CVPR.2017.243

  34. Unnikrishnan, A.; Sowmya, V.; Soman, K.: Deep learning architectures for land cover classification using red and near-infrared satellite images. Multimed. Tools Appl. 78(13), 18379–18394 (2019). https://doi.org/10.1007/s11042-019-7179-2

    Article  Google Scholar 

  35. Zhang, F.; Du, B.; Zhang, L.: Scene classification via a gradient boosting random convolutional network framework. IEEE Trans. Geosci. Remote Sens. 54(3), 1793–1802 (2016). https://doi.org/10.1109/tgrs.2015.2488681

    Article  Google Scholar 

  36. Shelhamer, E.; Long, J.; Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017). https://doi.org/10.1109/tpami.2016.2572683

    Article  Google Scholar 

  37. Alhassan, V.; Henry, C.; Ramanna, S.; Storie, C.: A deep learning framework for land-use/land-cover mapping and analysis using multispectral satellite imagery. Neural Comput. Appl. 32(12), 8529–8544 (2019). https://doi.org/10.1007/s00521-019-04349-9

    Article  Google Scholar 

  38. Fu, G.; Liu, C.; Zhou, R.; Sun, T.; Zhang, Q.: Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens. 9(5), 498 (2017). https://doi.org/10.3390/rs9050498

    Article  Google Scholar 

  39. Ronneberger, O.; Fischer, P.; Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015. Lecture Notes in Computer Science, vol. 9351 (2015). https://doi.org/10.1007/978-3-319-24574-4_28

  40. Badrinarayanan, V.; Kendall, A.; Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017). https://doi.org/10.1109/tpami.2016.2644615

    Article  Google Scholar 

  41. Singh, N.; Nongmeikapam, K.: Semantic segmentation of satellite images using Deep-Unet. Arab. J. Sci. Eng. (2022). https://doi.org/10.1007/s13369-022-06734-4

    Article  Google Scholar 

  42. Wang, Y.; Li, Z.; Zeng, C.; Xia, G.; Shen, H.: An urban water extraction method combining deep learning and google earth engine. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 769–782 (2020). https://doi.org/10.1109/jstars.2020.2971783

  43. Isikdogan, F.; Bovik, A.; Passalacqua, P.: Surface water mapping by deep learning. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10(11), 4909–4918 (2017). https://doi.org/10.1109/jstars.2017.2735443

  44. Feng, W.; Sui, H.; Huang, W.; Xu, C.; An, K.: Water body extraction from very high-resolution remote sensing imagery using deep U-Net and a superpixel-based conditional random field model. IEEE Geosci. Remote Sens. Lett. 16(4), 618–622 (2019). https://doi.org/10.1109/lgrs.2018.2879492

    Article  Google Scholar 

  45. Deoli, V.; Kumar, D.; Kumar, M.; Kuriqi, A.; Elbeltagi, A.: Water spread mapping of multiple lakes using remote sensing and satellite data. Arab. J. Geosci. (2021). https://doi.org/10.1007/s12517-021-08597-9

    Article  Google Scholar 

  46. Rouibah, K.; Belabbas, M.: Modeling and monitoring surface water dynamics in the context of climate changes using remote sensing data and techniques: case of Ain Zada Dam (North-East Algeria). Arab. J. Geosci. 15(9), 1–9 (2022). https://doi.org/10.1007/s12517-022-09910-w

    Article  Google Scholar 

  47. Shanmuga Priyaa, S.; Jeyakanthan, V.; Heltin Genitha, C.; Sanjeevi, S.: Estimation of water-spread area of Singoor Reservoir, southern India by super resolution mapping of multispectral satellite images. J. Indian Soc. Remote Sens. 46(1), 121–130 (2017). https://doi.org/10.1007/s12524-017-0666-x

    Article  Google Scholar 

  48. Ashtekar, A.; Mohammed-Aslam, M.; Moosvi, A.: Utility of normalized difference water index and GIS for mapping surface water dynamics in sub-upper Krishna Basin. J. Indian Soc. Remote Sens. 47(8), 1431–1442 (2019). https://doi.org/10.1007/s12524-019-01013-6

    Article  Google Scholar 

  49. Kulkarni, S.: Study of IRS 1C-LISS III image and identification of land cover features based on spectral responses. In: Geospatial World Forum, pp. 23–25 (2017)

  50. Tucker, C.: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8(2), 127–150 (1979). https://doi.org/10.1016/0034-4257(79)90013-0

    Article  Google Scholar 

  51. Yang, J.; Du, X.: An enhanced water index in extracting water bodies from Landsat TM imagery. Ann. GIS 23(3), 141–148 (2017). https://doi.org/10.1080/19475683.2017.1340339

    Article  Google Scholar 

  52. Herndon, K.; Muench, R.; Cherrington, E.; Griffin, R.: An assessment of surface water detection methods for water resource management in the Nigerien Sahel. Sensors 20(2), 431 (2020). https://doi.org/10.3390/s20020431

    Article  Google Scholar 

  53. Vignesh, T.; Thyagharajan, K.K.: Water bodies identification from multispectral images using Gabor filter, FCM and canny edge detection methods. In: International Conference on Information Communication and Embedded Systems (ICICES), pp. 1–5 (2017). https://doi.org/10.1109/ICICES.2017.8070767

  54. Chen, F.; Chen, X.; Van de Voorde, T.; Roberts, D.; Jiang, H.; Xu, W.: Open water detection in urban environments using high spatial resolution remote sensing imagery. Remote Sens. Environ. 242, 111706 (2020). https://doi.org/10.1016/j.rse.2020.111706

    Article  Google Scholar 

  55. Guo, G.; Wang, H.; Bell, D.; Bi, Y.; Greer, K.: KNN Model-Based Approach in Classification. Lecture Notes in Computer Science, Vol. 2888. Springer, Berlin (2003)

    Google Scholar 

  56. Rokach, L.; Maimon, O.: Top-down induction of decision trees classifiers: a survey. IEEE Trans. Syst. Man Cybern. C (Appl. Rev.) 35(4), 476–487 (2005). https://doi.org/10.1109/tsmcc.2004.843247

  57. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  58. Cortes, C.; Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  MATH  Google Scholar 

  59. 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 (2016). https://doi.org/10.48550/arXiv.1603.02754

  60. Fang, W.; Wang, C.; Chen, X.; Wan, W.; Li, H.; Zhu, S.; et al.: Recognizing global reservoirs from landsat 8 images: a deep learning approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(9), 3168–3177 (2019). https://doi.org/10.1109/jstars.2019.2929601

  61. Guo, H.; He, G.; Jiang, W.; Yin, R.; Yan, L.; Leng, W.: A multi-scale water extraction convolutional neural network (MWEN) method for GaoFen-1 remote sensing images. ISPRS Int. J. Geo-Inf. 9(4), 189 (2020). https://doi.org/10.3390/ijgi9040189

    Article  Google Scholar 

  62. News, C.; News, E.: Piles of garbage hinder Bhavanisagar dam water from reaching farmers | Erode News - Times of India. The Times of India. Retrieved 2 July 2022, from https://timesofindia.indiatimes.com/city/erode/piles-of-garbage-hinder-bhavanisagar-dam-water-from-reaching-farmers/articleshow/60989062.cms (2022)

  63. News, C.; News, C.: Flood alert sounded to people living along the banks of Bhavani River | Coimbatore News - Times of India. The Times of India. Retrieved 2 July 2022, from https://timesofindia.indiatimes.com/city/coimbatore/flood-alert-sounded-to-people-living-along-the-banks-of-bhavani-river/articleshow/64938858.cms (2022)

  64. Sandrp, V.: Krishnagiri Dam breach is wake call for dam safety in Tamil Nadu and elsewhere. Retrieved 11 September 2022, from https://sandrp.in/2017/12/01/krishnagiri-dam-breach-is-wake-call-for-dam-safety-in-tamil-nadu-and-elsewhere/#more-27575 (2022)

Download references

Acknowledgments

We thank National Remote Sensing Center (NRSC), Hyderabad, Indian Space Research Organization (ISRO), India, for providing the Resourcesat-2: LISS-III image data for educational purposes. We also thank the National Institute of Technology Puducherry, Karaikal, India, for providing research facilities in this area. We are grateful to the anonymous reviewers for their constructive comments and suggestions which improved the quality of this paper.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Puducherry, Karaikal, Puducherry, India

    Nagaraj Rajendiran & Lakshmi Sutha Kumar

Authors
  1. Nagaraj Rajendiran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lakshmi Sutha Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nagaraj Rajendiran.

Appendix

Appendix

See Figs. 5 and 6.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) 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

Rajendiran, N., Kumar, L. Pixel Level Feature Extraction and Machine Learning Classification for Water Body Extraction. Arab J Sci Eng 48, 9905–9928 (2023). https://doi.org/10.1007/s13369-022-07389-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-022-07389-x

Keywords

Search

Navigation