Importance of Mean Shift in Remote Sensing Segmentation

Abstract

The segmentation of an image can be considered as a preprocessing step for many of the algorithms such as object detection and identification.Many algorithms exist in the field of remote sensing for segmentation, in every case the desirable output of segmentation is a well defined region or features of object which can be distinguished from another.The desirable features include well based edges, gradients, textures etc.For a real world image the desirable features are difficult to identify.With the advancement of the high resolution imagery, the features which defined the object are too finely defined.It also contains noises which are due to fine edges, gradient variations and non-uniform textures.Segmentation algorithms are plenty in image processing which can be broadly categorized as edge based, color based and textured based.Edge based techniques fails due to the noise content.Textures are not uniform in real world images leading to problems of segmentation.Color based techniques need homogeneous regions which can distinguish objects, which will be difficult with the gradient variation.Defined in this paper is the importance of non-parametric clustering technique called Mean Shift, which in its inherent nature is able to cluster regions according to the desirable properties.The paper is a study on Mean-shift and its probable use in clustering of remote sensing imagery.Rather than a theoretical paper, the paper is arranged as an application based survey which can show the possible use and importance of mean shift in remote sensing.