Abstract
Hyperspectral imagery (HSI) unmixing is a process that decomposes pixel spectra into a collection of constituent spectra (endmembers) and their correspondent abundance fractions. Without knowing any knowledge of HSI data, the unmixing problem is transformed into a blind source separation (BSS) problem. Several methods have been proposed to deal with the problem, like independent component analysis (ICA). In this paper, we introduce spatial complexity that applies Markov random field (MRF) to characterize the spatial correlation information of abundance fractions. Compared to previous BSS techniques for HSI unmixing, the major advantage of our approach is that it totally considers HSI spatial structure. Additionally, a proof is given that spatial complexity is suitable for HSI unmixing. Encouraging results have been obtained in terms of unmixing accuracy, suggesting the effectiveness of our approach.
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Keywords
- Independent Component Analysis
- Hyperspectral Image
- Markov Random Field
- Independent Component Analysis
- Blind Source Separation
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References
Manolakis, D., Shaw, G.: Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine 19(1), 29–43 (2002)
Keshava, N.: A survey of spectral unmixing algorithms. Lincoln Lab Journal 14(1), 55–73 (2003)
Yuhas, R.H., Goetz, A.F.H., Boardman, J.W.: Discrimination among semi-arid landscape endmembers using the spectral angle mapper (sam) algorithm. In: Summaries of the 3rd Annual JPL Airborne Geoscience Workshop, vol. 1, pp. 147–149 (1992)
Cichocki, A., Amari, S.: Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications. John Wiley & Sons, Chichester (2002)
Bayliss, J.D., Gualtieri, J.A., Cromp, R.F.: Analyzing hyperspectral data with independent component analysis. In: Proceeding of SPIE Applied Image and Pattern Recognition Workshop, vol. 3240, pp. 133–143 (1997)
Chiang, S.-S., Chang, C.-I., Smith, J.A., Ginsberg, I.W.: Linear spectral random mixture analysis for hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing 40(2), 375–392 (2002)
Nascimento, J.M.P., Dias, J.M.B.: Does independent component analysis play a role in unmixing hyperspectral data? IEEE Transaction on Geoscience and Remote Sensing 43(1), 175–187 (2005)
Stone, J.V.: Independent Component Analysis: A Tutorial Introduction. MIT Press, Cambridge (2004)
Du, Q., Chakrarvarty, S.: Unsupervised hyperspectral image classification using blind source separation. In: IEEE International Conference on Acoustics, Speech, and Signal Processing 2003, vol. 3, pp. 437–440 (2003)
Deng, H., Clausi, D.A.: Unsupervised image segmentation using a simple mrf model with a new implementation scheme. Pattern Recognition 37(12), 2323–2335 (2004)
Chiang, S.-S., Chang, C.-I., Ginsberg, I.W.: Unsupervised target detection in hyperspectral images using projection pursuit. IEEE Transaction on Geoscience and Remote Sensing 39(7), 1380–1391 (2001)
Robila, S.A., Varshney, P.K.: Target detection in hyperspectral images based on independent component analysis. In: Proceeding of SPIE Automatic Target Recognition, vol. 4726, pp. 173–182 (2002)
Hyvärinen, A.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (1999)
The HYDICE HSI dataset, http://www.tec.army.mil/Hypercube/
Chang, C.-I., Du, Q.: Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing 42(3), 608–619 (2004)
Stone, J.V., Porrill, J.: Undercomplete independent component analysis for signal separation and dimension reduction. Technical report, Psychology Department, Sheffield University (1998), http://www.shef.ac.uk/~pc1jvs/
Xie, S., He, Z., Fu, Y.: A note on stone’s conjecture of blind signal separation. Neural Computation 17(2), 321–330 (2005)
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© 2006 Springer-Verlag Berlin Heidelberg
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Jia, S., Qian, Y. (2006). MRF Based Spatial Complexity for Hyperspectral Imagery Unmixing. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_58
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DOI: https://doi.org/10.1007/11815921_58
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