Cloud classification using the textural features of Meteosat images

The sum and difference histogram approach is applied to the assessment of the textural features of Meteosat images and the resulting textural parameters are used to classify the clouds appearing over North Africa. The images under consideration were taken by Meteosat in the visible and infrared bands during the month of December 1994. They cover North Africa, the Mediterranean Sea, Europe and the Atlantic Ocean. The visible images are first processed following four directions, namely 0degrees, 45degrees, 90degrees and 135degrees. For each direction, the textural parameters are calculated from the sum and difference histograms of grey levels. To take into account the effect of the texture anisotropy in the classification, the Karhunen-Loeve transformation (KLT) is then used. The minimum number of components representing these parameters is obtained with the slightest loss of information. Finally, after averaging the textural parameters over the four directions, each image of the database is divided into homogeneous classes by using the K-means algorithm. The approach thus described, tested on one of the images of the Brodatz album, shows that the classification ratio is greater than 96%. The segmentation of the Meteosat images is performed using both the textural parameters of visible images and the brightness of infrared images. It is then found that the different ground and cloud types are classified with proper accuracy. The implementation of this method to satellite images advantageously reduces the classification time, which is found to be three times smaller than that required by classical techniques of image processing.