DocumentCode
2937714
Title
Cluster-space classification: a fast k-nearest neighbour classification for remote sensing hyperspectral data
Author
Jia, Xiuping ; Richards, John A.
Author_Institution
Sch. of Electr. Eng., New South Wales Univ., Kensington, NSW, Australia
fYear
2003
fDate
27-28 Oct. 2003
Firstpage
407
Lastpage
410
Abstract
In this paper a fast k-nearest neighbour (k-NN) algorithm is presented which combines k-NN with a cluster-space data representation. Implementation of the algorithm is easier and classification time can be significantly reduced. Results from tests carried out with a Hyperion data set demonstrate that the simplification has little effect on classification performance and yet efficiency is greatly improved.
Keywords
data structures; pattern classification; pattern clustering; remote sensing; statistical analysis; Hyperion data set; classification time reduction; cluster space classification; data representation; fast K-nearest neighbour classification; remote sensing; statistical analysis; Australia; Bayesian methods; Clustering algorithms; Density functional theory; Educational institutions; Hyperspectral imaging; Hyperspectral sensors; Image classification; Pixel; Remote sensing;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop on
Print_ISBN
0-7803-8350-8
Type
conf
DOI
10.1109/WARSD.2003.1295222
Filename
1295222
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