Performance analysis of segmentation techniques for land cover types using remote sensing images

Information extraction is a very challenging task because remote sensing images are very complicated and can be influenced by many factors. The information we can derive from a remote sensing image mostly depends on the image segmentation results. Image segmentation is an important processing step in most image, video and computer vision applications. Extensive research has been done in creating many different approaches and algorithms for image segmentation. Labeling different parts of the image has been a challenging aspect of image processing. Various algorithms for automating the segmentation process have been proposed, tested and evaluated to find the most ideal algorithm to be used for different types of images. In this paper we explore segmentation techniques of satellite images using two algorithms - Quick Shift and Level Set. Quick Shift is a mode seeking algorithm which instead of iteratively shifting each point towards a local mean forms a tree of links to the nearest neighbor which increases the density. Level Set is a curve propagation algorithm based on the Partial-Differential Equation (PDE) method that provides a direct way to estimate the geometric properties of the evolving structure. The aim of this paper is to analyse the above two approaches and declare one of the two methods to be efficient for segmentation on remote sensing applications.