Title :
An adaptive band selection algorithm for dimension reduction of hyperspectral images
Author :
Xijun, Li ; Jun, Liu
Author_Institution :
Sch. of Remote Sensing & Inf. Eng., Wuhan Univ., Wuhan
Abstract :
An adaptive band selection algorithm for dimension reduction of hyperspectral images is proposed. Considering the spatial correlation and spectral correlation, a selection rule, referring to spectral information and its correlation, is constructed for band selection. To test the efficiency of this algorithm, K-means algorithm for unsupervised classification was applied on images generated from the algorithm. The results showed that the proposed algorithm reduced the computation amount and improved the classification accuracy.
Keywords :
correlation methods; geophysical signal processing; image classification; remote sensing; spectral analysis; K-means unsupervised classification algorithm; adaptive band selection rule algorithm; hyperspectral image dimension reduction; remote sensing; spatial correlation; spectral correlation; Data mining; Discrete wavelet transforms; Feature extraction; Hospitals; Hyperspectral imaging; Hyperspectral sensors; Partitioning algorithms; Principal component analysis; Remote sensing; Wavelet analysis; Adaptive Band Selection; Dimension Reduction; Hyperspetral Image; K-means; Remote Sensing;
Conference_Titel :
Image Analysis and Signal Processing, 2009. IASP 2009. International Conference on
Conference_Location :
Taizhou
Print_ISBN :
978-1-4244-3987-4
DOI :
10.1109/IASP.2009.5054596
