Reclustering hyperspectral data using variance-based criteria

Author

Bukhel, B. ; Rotman, Stanley R. ; Blumberg, D.G.

Author_Institution

Dept. of Electro-Opt., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel

fYear

2004

fDate

6-7 Sept. 2004

Firstpage

309

Lastpage

312

Abstract

We have examined the clustering results obtained via our previously published N-dimensional histogram segmentation algorithm. In particular, we have derived a method to recombine areas that have been oversegmented in the initial segmentation process. While the algorithm does reduce the number of clusters, different initial clustering inputs do lead to different clustering results. Methods to compare the different final segmentations will be discussed.

Keywords

geophysics computing; image resolution; image segmentation; pattern clustering; remote sensing; statistical analysis; N-dimensional histogram segmentation; clustering inputs; hyperspectral data reclustering; variance-based criteria; Clustering algorithms; Equations; Histograms; Hyperspectral imaging; Image segmentation; Stability;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Electronics Engineers in Israel, 2004. Proceedings. 2004 23rd IEEE Convention of

Print_ISBN

0-7803-8427-X

Type

conf

DOI

10.1109/EEEI.2004.1361153

Filename

1361153

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1983794