Robustness of Multiple Clustering Algorithms on Hyperspectral Images
Jason P. Williams
Broschiertes Buch

Robustness of Multiple Clustering Algorithms on Hyperspectral Images

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By clustering data into homogeneous groups, analysts can accurately detect anomalies within an image. This research was conducted to determine the most robust algorithm and settings for clustering hyperspectral images. Multiple images were analyzed, employing a variety of clustering algorithms under numerous conditions to include distance measurements for the algorithms and prior data reduction techniques. Various clustering algorithms were employed, including a hierarchical method, ISODATA, K-means, and X-means, and were used on a simple two dimensional dataset in order to discover potential ...