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- W2078507967 abstract "K-means clustering method has been employed in different applications of data analysis. This paper develops atarget detection system using the k-means algorithm including a preprocessing step based on the Euclideandistance. The pre-processing step reduces the computational complexity of the k-means algorithm in case ofhyperspectral imagery. After reducing the set of pixels in the background from the data by using the pre-processingstep, k-means algorithm is employed to determine the clusters in rest of the image data cube. Having obtained theclustered data, the objects of interest can easily be detected using the known target signature. The proposedclustering algorithm is successfully applied to the real life hyperspectral data sets where the objects of interest canefficiently be detected. The proposed scheme effectively reduces the convergence time of the k-mean algorithmcompared to that required by the traditional k-means algorithm." @default.
- W2078507967 created "2016-06-24" @default.
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- W2078507967 date "2007-04-09" @default.
- W2078507967 modified "2023-10-16" @default.
- W2078507967 title "Object detection in hyperspectral imagery by using K-means clustering algorithm with pre-processing" @default.
- W2078507967 doi "https://doi.org/10.1117/12.717926" @default.
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