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15 September 1993 Cloud type discrimination via multispectral textural analysis
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One of the primary interests in digital image processing is the development of robust methods to perform feature detection, extraction, and classification. Until recently, classification methods for cloud discrimination were mainly based on the spectral information of the imagery. However, because of the spectral similarities of certain features (such as ice clouds and snow) and the effects of atmospheric attenuation, multi-spectral rule based classifications do not necessarily produce accurate feature discrimination. Spectral homogeneity of two different features within a scene can lead to misclassification. Furthermore, the opposite problem can occur when one feature exhibits different spectral signatures locally but is homogeneous in its cyclic spatial variation. The exploration of spatial information is often advantageous in these discrimination problems. A texture-based method for feature identification has been investigated. This method uses a set of localized spatial filters known as two dimensional Gabor functions. Gabor filters can be described as a sinusoidal plane wave within a two-dimensional Gaussian envelope. The frequency and orientation of the sine plane and the width of the Gaussian envelope are determined by the Gabor parameters. These tunable channels yield joint optimal information both in the spatial and the frequency domains. The new method has been applied to the thermal channels of the NOAA-advanced very high resolution radiometer (AVHRR) data for cloud type discrimination.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Niloufar Lamei, Melba M. Crawford, Keith D. Hutchison, and Nahid Khazenie "Cloud type discrimination via multispectral textural analysis", Proc. SPIE 1934, Passive Infrared Remote Sensing of Clouds and the Atmosphere, (15 September 1993);


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