نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانش آموخته کارشناسی ارشد آبخیزداری، دانشگاه کاشان
2 دانشیار دانشکده منابع طبیعی و علوم زمین، دانشگاه کاشان
3 دانشجوی دکتری منابع آب، دانشگاه تربیت مدرس
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
In recent decades, land use changes cause an important environmental, economical and social damage in the world. Land use classification images are essential tools for land management and policy decisions. Fusion of remote sensing data could increase quality and intensify of data. The main aim of this study was investigated of land use classification algorithms (Support Vector Machine Background, Neural Net, and Maximum Likelihood) using a fusion of OLI remote sensing data using Geographic Information System (GIS). According to the results, Gram-Schmidt method had the best punctuality for taking of the firstly data and rebuilding of images. The results showed that Maximum likelihood method increased accuracy of PC_Sharpened and Gram-Schmidt algorithms. A significant accuracy in land use classification was observed via combination techniques.
کلیدواژهها [English]