BackgroundOne of the natural phenomena which have had considerable impacts on various regions of the world, including Iran, is “dust storm”. In recent years, this phenomenon has taken on new dimensions in Iran and has changed from a local problem to a national issue. This study is an attempt to investigate the formation of the dust storms crossing the Western Iran.MethodologyTo find the sources of the dust storms entering Iran, first we examine three determined dust paths in the region and their temporal activities, using MODIS satellite images. Then, four regions were identified as dust sources through soil, land cover and wind data. Finally, atmospheric analyses are implemented to find synoptic patterns inducing dust storms.Results and discussionSource 1 has covered the region between the eastern banks of Euphrates and western banks of Tigris. Source 2 is in desert area of western and south-western Iraq. Finally source 3 is bounded in eastern and south-eastern deserts of Saudi Arabia called Rub-Al-Khali desert, or Empty Quarter. Moreover, south-eastern part of Iraq (source 4) was also determined as a secondary source which thickens the dust masses originating from the above mentioned sources. The study of synoptic circulations suggests that the dust storms originating from source 1 are formed due to the intense pressure gradient between the low-pressure system of Zagros and a high-pressure cell formed on Mediterranean Sea. The dust events in sources 2 and 3 are outcomes of the atmospheric circulations dominant in the cold period of the year in mid-latitudes.
Hyperspectral image classification is among the most frequent topics of research in recent publications. This paper proposes a new supervised linear feature extraction method for classification of hyperspectral images using orthogonal linear discriminant analysis in both spatial and spectral domains. In fact, an orthogonal filter set and a spectral data transformation are designed simultaneously by maximizing the class separability. The important characteristic of the presented approach is that the proposed filter set is supervised and considers the class separability when extracting the features, thus it is more appropriate for feature extraction compared with other filters such as Gabor. In order to compare the proposed method with some existing methods, the extracted spatial-spectral features are fed into a support vector machine classifier. Some experiments on the widely used hyperspectral images, namely Indian Pines, Pavia University, and Salinas data sets, reveal that the proposed approach leads to state-of-the-art performance when compared to other recent approaches.
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