GEP  Vol.3 No.10 , December 2015
Automatic Generation of Water Masks from RapidEye Images
Abstract: Water is a very important natural resource and it supports all life forms on earth. It is used by humans in various ways including drinking, agriculture and for scientific research. The aim of this research was to develop a routine to automatically extract water masks from RapidEye images, which could be used for further investigation such as water quality monitoring and change detection. A Python-based algorithm was therefore developed for this particular purpose. The developed routine combines three spectral indices namely Simple Ratios (SRs), Normalized Green Index (NGI) and Normalized Difference Water Index (NDWI). The two SRs are calculated between the NIR and green band, and between the NIR and red band. The NGI is calculated by rationing the green band to the sum of all bands in each image. The NDWI is calculated by differencing the green to the NIR and dividing by the sum of the green and NIR bands. The routine generates five intermediate water masks, which are spatially intersected to create a single intermediate water mask. In order to remove very small waterbodies and any remaining gaps in the intermediate water mask, morphological opening and closing were performed to generate the final water mask. This proposed algorithm was used to extract water masks from some RapidEye images. It yielded an Overall Accuracy of 95% and a mean Kappa Statistic of 0.889 using the confusion matrix approach.
Cite this paper: Tetteh, G. and Schönert, M. (2015) Automatic Generation of Water Masks from RapidEye Images. Journal of Geoscience and Environment Protection, 3, 17-23. doi: 10.4236/gep.2015.310003.

[1]   McFeeters, S.K. (1996) The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features. International Journal of Remote Sensing, 17, 1425-1432.

[2]   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.

[3]   Feyisa, G.L., Meilby, H., Fensholt, R. and Proud, S.R. (2014) Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery. Remote Sensing of Environment, 140, 23-35.

[4]   Jiang, H., Feng, M., Zhu, Y., Lu, N., Huang, J. and Xiao, T. (2014) An Automated Method for Extracting Rivers and Lakes from Landsat Imagery. Remote Sensing, 6, 5067-5089.

[5]   Li, M., Xu, L. and Tang, M. (2011) An Extraction Method for Water Body of Remote Sensing Image Based on Oscillatory Network. Journal of Multimedia, 6, 252-260.

[6]   Terman, D. and Wang, D.L. (1995) Global Competition and Local Cooperation in a Network of Neural Oscillators. Physica D: Nonlinear Phenomena, 81, 148-176.

[7]   Mishra, K. and Prasad, P.R.C. (2014) Automatic Extraction of Water Bodies from Landsat Imagery Using Perceptron Model. Journal of Computational Environmental Sciences, 2015, 1-9.

[8]   Nawaz, N., Srinivasulu, S. and Rao, P.K. (2013) Automatic Extraction of Water Bodies Using Whole-R Method. International Journal of Environmental, Ecological, Geological and Geophysical Engineering, 7, 564-567.

[9]   Polidorio, A.M., Flores, F.C., Franco, C., Imai, N.N. and Tommaselli, A.M.G. (2010) Enhancement of Terrestrial Surface Features on High Spatial Resolution Multispectral Aerial Images. Proceedings of the 23rd SIBGRAPI Conference on Graphics, Patterns and Images, Rio Grande do Sul, 30 August-3 September 2010, 295-300.

[10]   Bochow, M., Heim, B., Kster, T., Roga, C., Bartsch, I., Segl, K. and Kaufmann, H. (2012) On the Use of Airborne Imaging Spectroscopy Data for the Automatic Detection and Delineation of Surface Water Bodies. In: Chemin, Y., Ed., Remote Sensing of Planet Earth, InTech, Rijeka, 3-22.

[11]   Ji, L., Zhang, L. and Wylie, B. (2009) Analysis of Dynamic Thresholds for the Normalized Difference Water Index. Photogrammetric Engineering and Remote Sensing, 75, 1307-1317.

[12]   Fu, J., Wang, J. and Li, J. (2008) Study on the Automatic Extrac-tion of Water Body from TM Image Using Decision Tree Algorithm. Proceedings of SPIE 6625, International Sympo-sium on Photoelectronic Detection and Imaging 2007: Related Technologies and Applications, Beijing, 9 September 2007, 1-9.

[13]   Nguyen, D.D. (2012) Water Body Extraction from Multi Spectral Image by Spectral Pattern Analysis. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B8, 181-186.

[14]   Zhaohui, Z., Prinet, V. and Songde, M. (2003) Water Body Extraction from Multi-Source Satellite Images. Proceedings of the IEEE International Geoscience and Re-mote Sensing Symposium, Toulouse, 21-25 July 2003, 3970-3972.

[15]   Rokni, K., Ahmad, A., Selamat, A. and Hazini, S. (2014) Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote Sensing, 6, 4173-4189.