Urban Arial Image Segmentation Using Spectral and Direction Features

In this paper textured image segmentation of urban areas using spectral and direction features is lionizing. Texture characterization specifically is very complex when the image data composed of several spectral bands at different wavelength. This topic is very important, especially in the case of remotely sensed hyperspectral images of urban areas, in which hundreds of spectral bands are often available. Since in various spectral band texture is different, we could not employ general methods of texture analysis to this images. On the other hand, most of textured analysis methods could not consider spectral and spatial features simultaneously. Thus, in order to extract features we use extended mathematical morphology. After constructing feature image, we employ region growing algorithm with respect to similarity between adjacent pixels. Before applying region growing algorithm we create a binary image. Results of this segmentation represent high performance of the proposed strategy.