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A method for analyzing atmospheric turbulence data is described in the form of a wavelet analysis which extracts localized structure in the form of discrete ramp-shaped gusts. A two-dimensional correlation surface is generated, the independent variables being position and scale, and discrete gusts are detected by the identification of peaks in this correlation surface which is equivalent to the invertible wavelet transform (WT). This method is illustrated by application to measured turbulence records. Implications of the results for aircraftdesign criteria, particularly for structural loads and load-alleviation control systems, are discussed.

A multiresolution method for analysing remotely sensed images is described, in which correlation filters, based on two-point differences, are applied over a range of scales with octave separation. When applied to typical Earth backgrounds viewed from space, the measured probability distributions of filter outputs exhibit strongly non-Gaussian statistics and satisfy scaling laws which allow a representation of the imagery in terms of fractal geometry. The method may be used as a basis for image, or image-region,characterization and, using the tails of the resulting normalised distributions, for the identification of those localized image features which are most unusual; that is, have lowest relative probability.

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