Remote sensing image classification based on improved fuzzy c-means

Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field

This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into a...

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 ...

Sar Image Classification Using Fuzzy C-means

Image Classification is the evolution of separating or grouping an image into different parts. The good act of recognition algorithms based on the quality of classified image. The good feat of recognition algorithms based on the quality of classified image. An important problem in SAR image application is accurate classification. Image segmentation is the mainly practical loom among virtually a...

Remote Sensing Image Classification

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