Guided kernel fuzzy c-means clustering with spatial information for remote-sensing image segmentation
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Fuzzy C-means (FCM) has widely been applied to computer vision, which emerged as an important tool for segmenting the structure of image data. However, the effectiveness of this technique lies in its inability to preserve edges and suppress noise, often leading to unsatisfactory segmentations. To solve this problem, we derive a modified FCM algorithm by using guided filter. The first key concept of our method is its linear translation-variant filtering process, which exploits edge-preserving smoothing property to preserve the edge structures in segmentation. The second is that this technique improves the robustness to noise by incorporating the spatial information into the objective function, which are obtained by the mean output of guided filtering. The main advantages of the proposed method are that it exhibits robustness to edge-preserving and noise and it can enhance the segmentation accuracy. Experimental results on both synthetic and real remote-sensing images suggest that the proposed method behaves well in segmentation performance.
Fuzzy C-Means, Kernelized Fuzzy C-Means, Guided Filter, Spatial Information, Image Segmentation