Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modi fied Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique

Abstract—Unsupervised Multi-Spectral Satellite Image Segmentation Combining Modi fied Mean-Shift and a New Minimum Spanning Tree Based Clustering Technique. An unsupervised object based segmentation, < Final Year Projects > combining a modified mean-shift (MS) and a novel minimum spanning tree (MST) based clustering approach of remotely sensed satellite images has been proposed in this correspondence. The image is first pre-processed by a modified version of the standard MS based segmentation which preserves the desirable discontinuities present in the image and guarantees oversegmentation in the output. A nearest neighbor based method for estimating the bandwidth of the kernel density estimator (KDE) and a novel termination condition have been incorporated into the standard MS. Considering the segmented regions as nodes in a low level feature space, an MST is constructed. An unsupervised technique to cluster a given MST has also been devised here. This type of hybrid segmentation technique which clusters the regions instead of image pixels reduces greatly the sensitivity to noise and enhances the overall segmentation performance. The superiority of the proposed method has been experimented on a large set of multi-spectral images and compared with some well-known hybrid segmentation models.