This research presents study using a satellite image of Landsat TM-5 to obtain a soil classified map for Northern Sinai using the supervised classification techniques. These techniques depend on taking the training areas (signatures) on the image from reference such as geological map for the same area. Three methods of supervised classification were used, (Minimum Distance, Mahalanobis Distance and Maximum Likelihood) with different number of classes. Firstly, the maximum number of classes (32) has been used and then reducing the number of classes in each classification gradually, by neglecting small areas of classes in the reference soil map in each stage. The number of classes used in these techniques was 32, 28, 24, 20 and 16, respectively using ENVI 4.4 software. The output results of classification techniques have been compared with the available geological map. The comparison indicates that the method of Maximum Likelihood gives the best results in all used number of classes. |
