Abstract
Accurate cloud detection is a key focus area for several researchers to determine the parameters of earth’s energy budget. It is a challenging task in the visible range due to resembling characteristics of thick clouds and snow/ice, difficulty in classifying faded texture soil and seasonal clouds (helpful for weather forecast), combined separation of vegetation and water against clouds. In this paper, a new color transformation approach is developed to classify thick clouds against three natural territories, i.e., water, vegetation, and soil. The proposed approach is implemented in two steps: preprocessing (includes color filter array interpolation method) and detection (a color transformation approach is introduced for classification). Extensive simulation studies are carried out on benchmark images collected from NOAA, VIRR, and MODIS databases. The superior performance of the proposed approach is demonstrated over the official cloud mask of VIRR database.
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Gupta, R., Nanda, S.J., Panchal, P. (2018). A Color Transformation Approach to Retrieve Cloudy Pixels in Daytime Satellite Images. In: Bhateja, V., Tavares, J., Rani, B., Prasad, V., Raju, K. (eds) Proceedings of the Second International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 712. Springer, Singapore. https://doi.org/10.1007/978-981-10-8228-3_28
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DOI: https://doi.org/10.1007/978-981-10-8228-3_28
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