Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering
Via Level Set Evolution
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Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven't kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images ...
Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven't kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak.
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