By increasing the spatial resolution of remote sensing imaging sensors, the image analyzing paradigm is
moving towards the object based image analysis approaches, instead of single pixels. Among the common
segmentation algorithms, super-pixel methods are presenting themselves as the new tools in computer vision.
In this paper, the capabilities of a state-of-the-art super-pixel algorithm, namely called SLIC, is investigated for
creating image segments from ultra-high resolution remote sensing images. In our proposed method, square
and hexagonal super-pixels were formed and then DBSCAN clustering algorithm is employed to build image
segments from these pixels. The results were compared to image segments obtained from FNEA algorithm, a
well-known method for remote sensing image segmentation. Visual and quantitative evaluations demonstrate
the efficiency of proposed method.