Clustering based background learning for hyperspectral anomaly detection

Author:

Ebrahim Aghili Mohamad,Imani MaryamORCID,Ghassemian HassanORCID

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences

Reference33 articles.

1. Arthur, D., Vassilvitskii, S., 2007. K-Means++: The Advantages of Careful Seeding. pp. 1027-1035.

2. Mapping target signatures via partial unmixing of AVIRIS data;Boardman,1995

3. Comparative evaluation of hyperspectral anomaly detectors in different types of background;Borghys;Proc. SPIE,2012

4. Estimation of number of spectrally distinct signal sources in hyperspectral imagery;Chein;IEEE Trans. Geosci. Remote Sens.,2004

5. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods,2000

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