Simultaneous Visualization and Segmentation of Hyperspectral Data Using Fuzzy K Means Clustering.
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2016, Vol 4, Issue 6
Abstract
Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes. The existing approaches uses optimization-based method for simultaneous fusion and unsupervised segmentation of hyperspectral remote sensing images by exploiting redundancy in the data. Then the weights are optimized to improve those statistical characteristics. The optimal recovery of the weight matrix additionally provides useful information in segmenting the hyperspectral data set spatially. But it is not suitable for multi spectral data set. In the proposed system uses fuzzy k-means clustering for simultaneous visualization and segmentation of hyperspectral data.
Authors and Affiliations
Dr. T. Arumuga Maria Devi, M. Mathan Raja
Keywords
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