Skip to main content
SpringerLink
Log in

An Adaptive Water Extraction Method from Remote Sensing Image Based on NDWI

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

Water is one of the most common and important objects on the earth, and its extraction is of great significance to many related researches in remote sensing domain. However, water always appears diversely, which makes its extraction not so simple. Many former methods are developed to extract water, which mainly based on a single model and only use spectral information, but the results are not so satisfying. An adaptive extraction method based on normalized difference water index (NDWI) is proposed here to extract water completely and accurately from remote sensing image. This study first compute NDWI to enhance water’s spectral information, and then it is redefined so as to use the modified histogram auto-segmentation method to initially separate water from background; next, after segmentation, water pixels can be searched out and are taken as seed points to proceed region growing to get the local area of water; last, the edge of the local area is searched by a window template, and iterative classification within it is employed to precisely extract water’s precise partition. Experiments are carried out here on an ETM+ image of a paralic area to extract water. Through comparison with other commonly used methods, it shows that the performance of the proposed method is superior to the others.

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

Similar content being viewed by others

References

  • Bazi, Y., & Melgani, F. (2006). Toward an optimal SVM classification system for hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 44, 3374–3385.

    Article  Google Scholar 

  • Chandranath, C., Rakesh, K., & Pankaj, M. (2003). Delineation of surface waterlogged areas in parts of bihar using IRS-1C LISS-III data. Journal of the Indian Society of Remote Sensing, 31(1), 57–65.

    Article  Google Scholar 

  • Delon, J., Desolneux, A., Lietrosani, J. L., & Petro, A. B. (2007). A nonparametric approach for histogram segmentation. IEEE Transactions on Image Processing, 16, 253–261.

    Article  Google Scholar 

  • Gao, B. C. (1996). NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.

    Article  Google Scholar 

  • Giada, S., De, G. T., Ehrlich, D., & Soille, P. (2010). Information extraction from very high resolution satellite imagery over Lukole refugee camp, Tanzania. International Journal of Remote Sensing, 24, 4251–4266.

    Article  Google Scholar 

  • Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing (2nd ed.). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Hagner, O., & Reese, H. (2007). A method for calibrated maximum likelihood classification of forest types. Remote Sensing of Environment, 110, 438–444.

    Article  Google Scholar 

  • Hu, D. Y., Zhao, W. L., Gong, H. L., Li, X J., & Li, J. C. (2008). Thematic information extraction from IKONOS imagery based on object and various features. In IGARSS2008 (pp. IV-814–IV-817). Boston, MA.

  • Jimenez, L. O., Rivera-Medina, J. L., Rodriguez-Diaz, E., Arzuaga-Cruz, E., & Ramirez-Velez, M. (2005). Integration of spatial and spectral information by means of unsupervised extraction and classification for homogenous objects applied to multispectral and hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, 844–851.

    Article  Google Scholar 

  • Lasaponara, R., & Masini, N. (2006). Identification of archaeological buried remains based on the normalized difference vegetation index (NDVI) from Quickbird satellite data. IEEE Geoscience and Remote Sensing Letters, 3, 325–328.

    Article  Google Scholar 

  • Li, J., & Narayanan, R. M. (2004). Integrated spectral and spatial information mining in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 42, 673–685.

    Article  Google Scholar 

  • Luo, J. C., Sheng, Y. W., Shen, Z. F., Li, J. L., & Gao, L. J. (2009). Automatic and high-precise extraction for water information from multispectral images with the step-by-step iterative transformation mechanism. Journal of Remote Sensing, 13, 610–622 [in Chinese].

    Google Scholar 

  • McFeeters, S. K. (1996). The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17, 1425–1432.

    Article  Google Scholar 

  • Miguel-Ayanz, J. S., & Biging, G. S. (1996). An iterative classification approach for mapping natural resources from satellite imagery. International Journal of Remote Sensing, 17, 957–981.

    Article  Google Scholar 

  • Miller, H. J. (2004). Tobler’s first law and spatial analysis. Annals of the Association of American Geographers, 94, 269–277.

    Article  Google Scholar 

  • Myint, S. W. (2008). An exploration of spatial dispersion, pattern, and association of socio-economic functional units in an urban system. Applied Geography, 28, 168–188.

    Article  Google Scholar 

  • Nath, S. K., & Dubois, E. (2006). An improved, wavelet-based, stereoscopic image sequence codec with SNR and spatial scalability. Signal Processing: Image Communication, 21(3), 181–199.

    Article  Google Scholar 

  • Ouma, Y. O., & Tateishi, R. (2006). A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data. International Journal of Remote Sensing, 27, 3153–3181.

    Article  Google Scholar 

  • Qin, A. K., & Clausi, D. A. (2010). Multivariate image segmentation using semantic region growing with adaptive edge penalty. IEEE Transactions on Image Processing, 19, 2157–2170.

    Article  Google Scholar 

  • Ren, K., Sun, H., Jia, Q., & Shi, J. (2009). Building recognition from aerial images combining segmentation and shadow. In Conf. ICIS 2009 IEEE Int. Conf., Shanghai (pp. 578–582).

  • Robert, A. S. (2007). Remote sensing: Models and methods for image processing (3rd ed.). Burlington: Elsevier.

    Google Scholar 

  • Rundquist, D., Lawson, M., Queen, L., & Cerveny, R. (1987). The relationship between the timing of summer-season rainfall events and lake-surface area. Water Resources Bulletin, 23, 493–508.

    Article  Google Scholar 

  • Xu, H. Q. (2006). Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing, 27, 3025–3033.

    Article  Google Scholar 

  • Yu, Q. Y., & Clausi, D. A. (2008). IRGS: image segmentation using edge penalties and region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30, 2126–2139.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by National Natural Science Foundation of China under Grants 40971228 and 40871203, and Major Projects on Control and Rectification of Water Body Pollution of China under Grant 2009ZX07318-001.

Author information

Authors and Affiliations

  1. Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, 100101, People’s Republic of China

    Cheng Qiao, Jiancheng Luo, Zhanfeng Shen & Zhiwen Zhu

  2. Department of Geography, University of California, Los Angeles, CA, 90095-1524, USA

    Yongwei Sheng

  3. School of Information Engineering, China University of Geosciences (Beijing), Beijing, 10083, People’s Republic of China

    Dongping Ming

Authors
  1. Cheng Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiancheng Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yongwei Sheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhanfeng Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhiwen Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Dongping Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Cheng Qiao.

About this article

Cite this article

Qiao, C., Luo, J., Sheng, Y. et al. An Adaptive Water Extraction Method from Remote Sensing Image Based on NDWI. J Indian Soc Remote Sens 40, 421–433 (2012). https://doi.org/10.1007/s12524-011-0162-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-011-0162-7

Keywords

Search

Navigation