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Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data

Comparison of techniques for measuring cloud texture in remotely sensed satellite meteorological image data

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© The Institution of Electrical Engineers
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This investigation has attempted to discover appropriate texture descriptors and to reveal more clearly the importance of texture analysis techniques for multispectral cloud classification. The textural features considered in this study include both spatial and frequency features. The spatial features were mainly those based on spatial grey-level difference statistics and circular Moran autocorrelation measures. The frequency features were those based on summed energies of polar co-ordinate Fourier power spectra and entropy-based measures of the spatial distribution of frequency entries in the polar spectra. Some other textural features, such as the Roberts gradient measure, were also investigated. The work was performed with TIROS-N AVHRR image data acquired in the late spring of 1979 over areas near the British Isles. The results of the evaluation and corresponding conclusions show which individual, and group of textural features, appear the most appropriate for aiding multispectral cloud classification.

Inspec keywords: pattern recognition; clouds; remote sensing; atmospheric techniques; picture processing

Other keywords: spatial grey-level difference statistics; cloud texture; AD 1979; British Isles; atmospheric techniques; Moran autocorrelation measures; TIROS-N AVHRR image data; polar coordinate Fourier power spectra; texture descriptors; multispectral cloud classification; remotely sensed satellite meteorological image data

Subjects: Atmospheric, ionospheric and magnetospheric techniques and equipment; Cloud physics; Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research; Optical information, image and video signal processing

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