A Comparative Study of Fuzzy Techniques Used for High Resolution Imagery
Keywords:
Adaptive Fuzzy C-Means, Modified Fuzzy C-Means, Texture Segmentation, region mergingAbstract
Image segmentation is crucial to object-oriented remote sensing imagery analysis. A novel texture segmentation algorithm is proposed for high-resolution remote sensing imagery, in which texture clustering is first carried out as loose constraint for later segmentation. The algorithm is based on region adjacency graph models of region adjacency graph, which can achieve fast node merging, defending on the global optimum. Here the spectral, texture and shape features, is established to measure the similarity between nodes and gives the same semantic descriptions for the texture objects. During the merging process, optimal sequence merging interacts with texture clustering to refine the real edges of a texture region. This algorithm cannot only merge the homogenous texture segments with spectral variability easily but can also detect the real object boundaries well. It found that the execution time of modified Fuzzy clustering techniques decreases the number of clusters increases. But in the other techniques the execution time increases when the numbers of clusters increases and detect the boundaries not well. The Modified Fuzzy Techniques detect the hidden details with more accuracy.
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