17 January 2002 Applying reconfigurable hardware to the analysis of multispectral and hyperspectral imagery
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Abstract
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 implementation in reconfigurable hardware processed 10 channel multispectral images two orders of magnitude faster than a software implementation of the same algorithm. The advantage of using reconfigurable hardware to accelerate k-means clustering is clear; the disadvantage is the hardware implementation worked for one specific dataset. It is a non-trivial task to change this implementation to handle a dataset with different number of spectral channels, bits per spectral channel, or number of pixels; or to change the number of clusters. These changes required knowledge of the hardware design process and could take several days of a designer's time. Since multispectral data sets come in many shapes and sizes, being able to easily change the k-means implementation for these different data sets is important. For this reason, we have developed a parameterized implementation of the k-means algorithm. Our design is parameterized by the number of pixels in an image, the number of channels per pixel, and the number of bits per channel as well as the number of clusters. These parameters can easily be changed in a few minutes by someone not familiar with the design process. The resulting implementation is very close in performance to the original hardware implementation. It has the added advantage that the parameterized design compiles approximately three times faster than the original.
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Miriam E. Leeser, Miriam E. Leeser, Pavle Belanovic, Pavle Belanovic, Michael Estlick, Michael Estlick, Maya Gokhale, Maya Gokhale, John J. Szymanski, John J. Szymanski, James P. Theiler, James P. Theiler, } "Applying reconfigurable hardware to the analysis of multispectral and hyperspectral imagery", Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); doi: 10.1117/12.453329; https://doi.org/10.1117/12.453329
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