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19 February 2013 Decomposition of satellite-derived images for the distinction of cloud types' features
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Proceedings Volume 8655, Image Processing: Algorithms and Systems XI; 865508 (2013)
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
Linear filtering methods using convolution techniques are applied in computer vision, to detect spatial discontinuities in the intensity of luminance of photograph images. These techniques are based on the following principal: a pixel’s neighborhood contains information about its intensity. The variation of this intensity provides some information about the distribution and the possible decomposition of the image in various features. This decomposition relies on the relative position of the pixel (edge or not) on the image. These principals, integrated into remote sensing analyses, are applied in this study to differentiate cloud morphological features representing cloud types from a thermal image product (the Cloud top temperatures) derived from polar orbit satellites’ observations. This approach contrast with that of other techniques commonly used in satellite cloud classification, and based on optical or thermodynamic properties of the clouds. The interpretation of the distribution of these cloud morphological features, and their frequency is evaluated against another cloud classification method relying on cloud optical properties. The results show a relatively good match between the two classifications. Implications of these results, on the estimation of the impact of cloud shapes’ variations on the recent climate are discussed.
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Jules R. Dim and Hiroshi Murakami "Decomposition of satellite-derived images for the distinction of cloud types' features", Proc. SPIE 8655, Image Processing: Algorithms and Systems XI, 865508 (19 February 2013);

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