The output map units from SOM are just numbers and need to be analyzed and interpreted. iterations shows the best performance for yardang identification. The map with initial radius of 3, final neighborhood radius of 0. Learning of the SOM was performed with four morphometric parameters as the inputs and a two-dimensional output of 10 neurons. Two dimensional plots of mean values of morphometric parameters (feature space), oblique views of map units draped over the DEM, Landsat ETM+ data and high resolution QuickBird images were used to study the mega yardangs in other places than Iran. Frequency histograms and average quantization error of the results are compared. The derived morphometric parameters were used as an input to SOM. A local window of 5×5 is passed over the DEM and slope, cross- sectional curvature, maximum and minimum curvatures are derived by fitting a bivariate quadratic approximation surface. Yardang identification and analysis in the study area was performed using the parameters proposed by Wood (1996a). Wood (1996a) defined a set of criteria to classify DEMs into morphometric classes. To calculate the morphometric features, a local window is passed over the SRTM DEM and the change in gradient of a central point in relation to its neighbors is derived by a bivariate quadratic function. The second order derivatives of DEM are affected by geomorphological processes (Evans, 1972 Wood, 1996b). In geomorphic studies of landscapes, the first and second order derivatives of DEM are the basic components for morphometric analysis (Evans, 1972). Self Organizing Map (SOM) is an unsupervised and nonparametric artificial neural network algorithm that clusters high dimensional input vectors into low dimensional (usually two dimensional) output map which preserve topology of the input data. That was re-projected to UTM grid with WGS84 Datum.We used 90 m DEM produced from version 3 SRTM 3 arc second data and the SOM algorithm for identification of yardangs in the western part of Lut desert. In this study 3 arc second DEM of version 3.0 SRTM data (~ 90 m) with geographic projection acquired was used. sandstones, ignimbrites, limestones and basement rocks by a relatively unimodal wind direction. According to Goudie, these features develop in a wide range of rock types e.g. Goudie (2007) identified mega-yardangs in hyper-arid environments with total rainfall less than 50 mm including central Asia, the Lut desert in Iran, northern Saudi Arabia, Bahrain, the Libyan Desert in Egypt, the central Sahara, the Namib desert, the high Andes and Peruvian desert. A limited number of morphometric investigations have been done on yardangs. Yardangs are streamlined forms up to 150 km long and 75 m in height resulting from a number of formative processes, including wind abrasion, deflation, fluvial incision, desiccation cracks, slumping, weathering and mass movement (Goudie, 2007 McCauley et al., 1977 Ward and Greeley, 1984). With an area of about 80,000 square km it is regarded to the hottest and the driest desert in the world (Alavi Panah et al., 2007 Gabriel, 1938 Mildrexler et al., 2006). The Lut desert (Dasht-e Lut) in the south east of Iran is described as the “thermal pole of the Earth” (Mildrexler et al., 2006). Hence the mega-yardangs with tens meter high and hundred meters long are easily identifiable on satellite images and their global distribution and properties can be mapped. QuickBird provide useful information of remote area. The recent advances in the remote sensing technique and easily available of high resolution satellite data e.g. Yardangs due to intensive wind erosion are exclusive landforms on the earth's desert and possibly occur on Mars and Venus.
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