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Remote sensing image segmentation based on ant colony optimized fuzzy C-means clustering
| Content Provider | Semantic Scholar |
|---|---|
| Author | Yan, Jingfeng |
| Copyright Year | 2014 |
| Abstract | Middle spatial resolution multi-spectral remote sen sing image is a kind of color image with low contra st, fuzzy boundaries and informative features. In view of the se features, the fuzzy C-means clustering algorithm is an ideal choice for image segmentation. However, fuzzy C-mea ns clustering algorithm requires a pre-specified nu mber of clusters and costs large computation time, which is easy to fall into local optimal solution. In order to overcome these shortcomings, ant colony algorithm is employe d to optimize fuzzy C-means algorithm in remote sen sing image segmentation. First, the centers and number of clus ters is determined by ant colony optimization algor ithm. Then the initialization fuzzy C-means algorithm is used for remote sensing image classification. Experimental r esults show that the ant colony optimization is an effective me thod to solve the problem of fuzzy C-means algorith m in remote sensing image segmentation and the visual interpret ation of segmentation is much improved by proposed ant colony optimized C-means clustering. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://jocpr.com/vol6-iss6-2014/JCPR-2014-6-6-2675-2679.pdf |
| Alternate Webpage(s) | http://www.jocpr.com/articles/remote-sensing-image-segmentation-based-on-ant-colony-optimized-fuzzy-cmeans-clustering.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
