SPATIAL-SPECTRAL FUZZY K-MEANS CLUSTERING FOR REMOTE SENSING IMAGE SEGMENTATION

Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

Article history: Received 24 October 2008 Received in revised form 18 March 2009 Accepted 18 April 2009

Spatial Kernel K-Harmonic Means Clustering for Multi-spectral Image Segmentation

The problem of image segmentation using intensity clustering approaches has been addressed in the literature. Grouping pixels of similar intensity to form clusters in an image has been tackled using a number of methods, such as the K-Means (KM) algorithm. The K-Harmonic Means (KHM) was proposed to overcome the sensitivity of KM to centre initialisation. In this paper, we investigate the use of ...

K - Means Clustering for Automatic Image Segmentation

Color Image Segmentation Using a Spatial K-Means Clustering Algorithm

This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In additi...

Remote sensing image segmentation based on ant colony optimized fuzzy C-means clustering

Middle spatial resolution multi-spectral remote sensing image is a kind of color image with low contrast, fuzzy boundaries and informative features. In view of these features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-means clustering algorithm requires a pre-specified number of clusters and costs large computation time, which is easy to ...