سمیه طحان پور - دانشجو ارشد موسسه آموزش عالی خاوران
محمد رضا مباشری - هیات علمی موسسه آموزش عالی خاوران
محمد رضا مباشری - هیات علمی موسسه آموزش عالی خاوران

In recent years, extraction of information from hyperspectral images has been the target of many research studies in remote sensing. Some of this information includes the type and amount of coating surface material. Determining the type of materials through their interaction with differentregions of the electromagnetic spectrum is possible and determination of their quantities depend on dimensions of pixcels.Fusing hyperspectral data along with high spatial resolution data allow concurrent access to information in appropriate scale. In land cover monitoring air pollution and atmospheric emissions studies, the need for researchers to use hyperspectral image data. Although images such as GeoEye-1 on Google Earth database and Ikonos image enable to determine spatial information with high precision, but due to their low spectral resolution they don’t have potential for accurate diagnosis of urben area.In this study in order to enhance the spectral data, high spatial resolution (GeoEye-1) along with high spectral resolution images (Hyperion) have been deployed. In this study different classes have been extracted from Google Earth image with spatial resolution of 1.65 where an nsupervised classification using two-step separation of pure components for higher accuracy has been carried out.Using hyperspectral images and solving the mixing equations inversty, the reflectance of different classes were calculated

Google Earth, spatial resolution, spectral resolution, covering urban