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22 September 2011 Multiclassification of objects in cloudy environments
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A two-step procedure for the reliable recognition and multiclassication of objects in cloudy environments is proposed. The input scene is preprocessed with the help of an iterative algorithm to remove the effects of the cloudy environment, followed by a complex correlation filtering for the multiclassication of target objects. The iterative algorithm is based on a local heuristic search inside a moving window using a nonlinear signal model for the input scene. The preprocessed scene is correlated with a multiclass correlation filter based in complex synthetic discriminant functions. Computer simulation results obtained with the proposed approach in cloudy images are presented and discussed in terms of different performance metrics.
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Oliver G. Campos Trujillo, Francisco J. Ramirez Arias, and Victor H. Diaz-Ramirez "Multiclassification of objects in cloudy environments", Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 81350C (22 September 2011);

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