Miriam Leeser, James Theiler, Michael Estlick, Natasha Kitaryeva, John J. Szymanski
We investigate the effect of truncating the precision of hyperspectral image data for the purpose of more efficiently segmenting the image using a variant of k-means clustering. We describe the...
Miriam Leeser, Pavle Belanovic, Michael Estlick, John J. Szymanski, James Theiler
Unsupervised clustering is a powerful technique for processing multispectral and hyperspectral images. Last year, we reported on an implementation of k-means clustering for multispectral images. Our...
Design Tradeoffs in a Hardware Implementation of the k-Means Clustering Algorithm (2000)
Miriam Leeser, James Theiler, Michael Estlick, John J. Szymanski
Hyperspectral imagery provides exquisitely detailed information, but poses a serious challenge to the image analyst. Massive quantities of data must be reduced in a way that identifies useful...
James Theiler, Miriam Leeser, Michael Estlick, John J. Szymanski
Modern hyperspectral imagers can produce data cubes with hundreds of spectral channels and millions of pixels. One way to cope with this massive volume is to organize the data so that pixels with...