Generally fuzzy c-means algorithm is one proved that very well suited for remote sensing image segmentation, exhibited sensitivity to the initial guess with regard to both speed and stability. But it also showed sensitivity to noise. This paper proposes a fully automatic technique to obtain image clusters. A modified fuzzy c-means classification algorithm is used to provide a fuzzy partition. This method is less sensitive to noise as it filters the image while clustering it, which is bgsed on the consideration of the neighbors as factors the attract pixels into their cluster. The experimental results on JERS-I Synthetic Aperture Radar (SAR) image demonstrate its potential usefulness.
In this paper, a multispectral image edge detection algorithm is proposed based on the idea that uses global multispectral information to guide local gradient computation. The image is first segmented into a small number of clusters through a clustering algorithm. According to these clusters, a set of linear projection vectors are generated. For a given image, if n clusters are found, there are n(n-1)/2 possible projection vectors. Edge detection is performed by calculating gradient magnitudes separately on each channel. An appropriate projection vector is chosen for each pixel to maximize gradient magnitude. In this way, edges are treated as transitions from one cluster to another. The algorithm has been tested on JERS-UOPS images, and the experimental results demonstrate its potential usefulness.
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