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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. http://dx.doi.org/10.5772/34073

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• TITLE: Automatic Generation of Water Masks from RapidEye Images

  AUTHORS: Gideon Okpoti Tetteh, Maurice Schönert

  KEYWORDS: Water Mask, Image Threshold, Simple Ratio, Normalized Green Index, Normalized Difference Water Index, Logical and Morphological Operations, RapidEye

  JOURNAL NAME: Journal of Geoscience and Environment Protection, Vol.3 No.10, December 18, 2015

  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.