SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means
An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Hindawi Limited
2018-01-01
|
| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2018/4576015 |
| Summary: | An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art. |
|---|---|
| ISSN: | 1024-123X 1563-5147 |