Abstract
Independent Component Analysis (ICA) is a blind source separation method which is exploited for various applications in signal processing. In hyperspectral imagery, ICA is commonly employed for detection and segmentation purposes. But it is often thought to be unable to quantify abundances. In this paper, we propose an ICA-based method to estimate the anomaly abundances from the independent components. The first experiments on synthetic and real world hyperspectral images are very promising referring to the estimation accuracy and robustness.
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References
Cardoso, J.F.: Blind signal separation: statistical principles. Proceedings of the IEEE 9, 2009–2025 (1998)
Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley-Interscience, Chichester (2001)
Wang, J., Chang, C.I.: Applications of independent component analysis (ica) in endmember extraction and abundance quantification for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 44, 2601–2616 (2006)
Chang, C.: Hyperspectral Imaging: techniques for spectral detection and classification. Kluwer academic/ Plenium publishers, New york (2003)
Chang, C.I.: Estimation of the number of spectral sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing 42 (2004)
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© 2007 Springer-Verlag Berlin Heidelberg
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Huck, A., Guillaume, M. (2007). Independent Component Analysis-Based Estimation of Anomaly Abundances in Hyperspectral Images. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_15
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DOI: https://doi.org/10.1007/978-3-540-74607-2_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74606-5
Online ISBN: 978-3-540-74607-2
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