Abstract
Linear Spectral Unmixing (LSU) has been proposed for the analysis of hyperspectral images, to compute the fractional contribution of the detected endmembers to each pixel in the image. In this paper we propose that the fractional abundance coefficients to be used as features for the supervised classification of the pixels. Thus we compare them with two well-known linear feature extraction algorithms: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). A specific problem of LSU is the determination of the endmembers, to this end we employ two approaches, the Convex Cone Analysis and another one based on the detection of morphological independence.
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References
Fukunaga, K.: Introduction to statistical pattern recognition. Academic Press, Boston, MA (1990)
Graña, M., Gallego, J.: Associative Mophological Memories for endmember induction. In: Proc. IGARSS 2003, Tolouse, France (2003)
Graña, M., Gallego, J., Hernandez, C.: Further results on AMM for endmember induction. In: IEEE 2003 Workshop on Advances in Techniques for Analysis of Remotely Sensed Data (2003)
Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sciences 79, 2554–2558 (1982)
Hyvarynën, A., Oja, E.: A fast fixed-point algorithm for independent component analysis. Neural Comp. 9, 1483–1492 (1999)
Hyvarynën, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)
Ifarraguerri, A., Chang, C.-I.: Multispectral and Hyperspectral Image Analysis with Convex Cones. IEEE Trans. Geos. Rem. Sensing 37(2), 756–770 (1999)
Keshava, N., Mustard, J.F.: Spectral unimixing. IEEE Signal Proc. Mag. 19(1), 44–57 (2002)
Ritter, G.X., Diaz-de-Leon, J.L., Sussner, P.: Morphological bidirectional associative memories. Neural Networks 12, 851–867 (1999)
Ritter, G.X., Sussner, P., Diaz-de-Leon, J.L.: Morphological associative memories. IEEE Trans. on Neural Networks 9(2), 281–292 (1998)
Ritter, G.X., Urcid, G., Iancu, L.: Reconstruction of patterns from moisy inputs using morphological associative memories. J. Math. Imaging and Vision 19(2), 95–112 (2003)
Sussner, P.: Observations on Morphological Associative Memories and the Kernel Method. In: Proc. IJCNN 2001, Washington DC (July 2001)
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Graña, M., D’Anjou, A. (2004). Feature Extraction by Linear Spectral Unmixing. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3213. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30132-5_95
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DOI: https://doi.org/10.1007/978-3-540-30132-5_95
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23318-3
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