Dependent Component Analysis for Hyperspectral Image Classification (CROSBI ID 556568)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Du, Qian ; Kopriva, Ivica
engleski
Dependent Component Analysis for Hyperspectral Image Classification
Independent component analysis (ICA) has been widely used for hyperspectral image classification in an unsupervised fashion. It is assumed that classes are statistically mutual independent. In practice, this assumption may not be true. In this paper, we apply dependent component analysis (DCA) to unsupervised classification, which does not require the class independency. The basic idea of our DCA approaches is to find a transform that can improve the class independency but leave the basis mixing matrix unchanged ; thus, an original ICA method can be employed to the transformed data where classes are less statistically dependent. Linear transforms that possess such a required invariance property and generate less dependent sources include: high-pass filtering, innovation, and wavelet transforms. These three transforms correspond to three different DCA algorithms, which will be investigated in this paper. Preliminary results show that the DCA algorithms can slightly improve the classification accuracy.
Dependent Component Analysis. Independent Component Analysis. Hyperspectral Imagery. Classification.
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Podaci o prilogu
74770G-1-7470G-8.
2009.
objavljeno
Podaci o matičnoj publikaciji
Proceddings of SPIE-Volume 7477
Lorenzo Bruzzone, Claudia Notarnicola, Francesco Posa
Bellingham (WA): SPIE
9780819477828
Podaci o skupu
9780819477828
predavanje
31.08.2009-03.09.2009
Berlin, Njemačka