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Comparison of Texture Analysis Techniques in Both Frequency and Spatial Domains for Cloud Feature Extraction

Abstract : Identification of cloud through cloud classification using satellite observations is yet to produce consistent and dependable results. Cloud types are too varied in their geophysical parameters, as measured by satellite remote sensing instruments, to provide for a direct accurate classification. To aid in classification, texture measures are additionally employed. These measures characterize local spectral variations in images. They are widely used for image segmentation, classification, and edge detection. Numerous methods have been developed to extract textural features from an image on the basis of spatial and spectral properties of the image. In our effort, several of these methods are studied for their applicability in cloud classification and cloud feature identification. These examined texture methods include (a) spatial gray-level co-occurrence matrices, (b) gray-level difference vector method, and (c) a class of filters known as Gabor transforms. Methods (a) and (b) are spatial and statistical while method (c) is in the frequency domain. A series of comparative tests have been performed applying these methods to NOAA-AVHRR satellite data. A discussion as to the suitability of these texture methods for clout classification concludes this study.