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Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment

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DOI: 10.23977/geors.2019.21002 | Downloads: 11 | Views: 384

Author(s)

Pierre Delmas 1, Caroline Fossati 1, Salah Bourennane 1

Affiliation(s)

1 Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France

Corresponding Author

Pierre Delmas

ABSTRACT

Target detection is an important issue in the HyperSpectral Image (HSI) processing field. However, current spectral-identification-based target detection algorithms are sensitive to the noise and most denoising algorithms cannot preserve small targets, therefore it is necessary to design a robust detection algorithm that can preserve small targets. Because the signal-dependent photonic noise has become as dominant as the signal-independent noise generated by the electronic circuitry in HSI data collected by new-generation hyperspectral sensors, the reduction of the additive signal-dependent photonic noise becomes the focus of the current research in this field. To reduce the opto-elctronic noise from HSIs, a new method is developed in this paper. Firstly, a pre-whitening procedure is proposed to whiten noise in HSIs. Secondly, a three-dimensional wavelet packet transform (3-WPT) in tensor form is presented to find different component tensors of HSI. Then, to jointly filter a component tensor in each mode, multiway Wiener filter (MWF) is introduced. Moreover, to determine the best transform level and basis of 3-WPT a risk function is proposed. The effectiveness of our method in classification is experimentally demonstrated on a real-world HSI acquired by airborne sensor.

KEYWORDS

Detection, multi-linear algebra, reduction, wavelet, hyperspectral image

CITE THIS PAPER

Pierre Delmas, Caroline Fossati and Salah Bourennane, Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment, Geoscience and Remote Sensing (2019) Vol. 2: 25-40. DOI: http://dx.doi.org/10.23977/geors.2019.21002.

REFERENCES

[1] Z. Liu, H. Wang, and Q. Li. Tongue tumor detection in medical hyperspectral images. Sensors 1 (2011) 162–174.
[2] S. Lewis, A. Hudak, R. Ottmar, P. Robichaud, L. Lentile, S. Hood, J. Cronan, and P. Morgan. Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of alaska, usa. Int. J. Wildland Fire 2 (2011) 255–271.
[3] D. Landgrebe. Hyperspectral image data analysis. IEEE Signal Process. Mag. 1 (2002) 17–28.

[4] D. Muti and S. Bourennane. Survey on tensor signal algebraic filtering. Signal Process. 2 (2007) 237–249.

[5] J. Marot, C. Fossati, and S. Bourennane. Overview on advances in tensor data denoising methods. SIAM J. Matrix Anal. Appl. 3 (2008) 1172–1204.

[6] A. Soltani-Farani, H. R. Rabiee, and S. A. Hosseini. Spatial-aware dictionary learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 1 (2015) 527–541.

[7] J. Kerekes and J. Baum. Hyperspectral imaging system modeling. Linc Lab. J. 1 (2003) 117–130.

[8] J. Kerekes and J. Baum. Full-spectrum spectral imaging system analytical model. IEEE Trans. Geosci. Remote Sens. 3 (2005) 571–580.

[9] B. Aiazzi, L. Alparone, A. Barducci, S. Baronti, P. Marcoinni, I. Pippi, and M. Selva. Noise modelling and estimation of hyperspectral data from airborne imaging spectrometers. Ann. Geophys. 1 (2006) 1–9.

[10] E. Christophe, D. Leger, and C. Mailhes. Quality criteria benchmark for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 9 (2005) 2103–2114.

[11] N. Acito, M. Diani, and G. Corsini. Signal-dependent noise modeling and model parameter estimation in hyperspectral images. IEEE Trans. Geosci. Remote Sens. 8 (2011) 2957–2971.

[12] M. L. Uss, B. Vozel, V. V. Lukin, and K. Chehdi. Local signal-dependent noise variance estimation from hyperspectral textural images. IEEE J. Sel. Topics Signal Process. 3 (2011) 469–486.

[13] L. He, J. Li, C. Liu and S. Li. Recent Advances on Spectral-Spatial Hyperspectral Image Classification: An Overview and New Guidelines. IEEE Trans. Geosci. Remote Sens. 3 (2018) 1579–1597.

[14] N. Renard, S. Bourennane, and J. Blanc-Talon. Denoising and dimensionality reduction using multilinear tools for hyper-spectral images. IEEE Geosci. Remote Sens. Lett. 2 (2008) 138–142.
[15] D. Landgrebe. Signal theory methods in multispectral remote sensing. New Jersey: Wiley, (2003).

[16] L. Alparone, M. Selva, B. Aiazzi, S. Baronti, F. Butera, and L. Chiarantini. Signal-dependent noise modelling and estimation of new-generation imaging spectrometers. Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 09 ( 2009) 1–4.

[17] D. Muti and S. Bourennane. Multidimensional filtering based on a tensor approach. Signal Process. 12 (2005) 2338–2353.
[18] D. Muti and S. Bourennane. Multiway filtering based on fourth-order cumulants. EURASIP Appl. Signal Process. 7 (2005) 1147–1158.

[19] D. Muti, S. Bourennane, and J. Marot. Lower-rank tensor approximation and multiway filtering. SIAM J. Matrix Anal. Appl. 3 (2008) 1172–1204.

[20] D. Letexier and S. Bourennane. Noise removal from hyperspectral images by multidimensional filtering. IEEE Trans. Geosci. Remote Sens. 7 (2008) 2061–2069.

[21] L. De Lathauwer, B. De Moor, and J. Vandewalle. On the best rank-1 and rank-(r1, r2,..., rn) approximation of higher-order tensors. SIAM J. Matrix Anal. Appl. 4 (2000) 1324–1342.

[22] L. De Lathauwer and J. Vandewalle. Dimensionality reduction in higher-order signal processing and rank-(r1, r2,..., rn) reduction in multilinear algebra. SIAM J. Matrix Anal. Appl. 4 (2000) 1324–1342.

[23] N. Renard and S. Bourennane. Improvement of target detection methods by multiway filtering. IEEE Trans. Geosci. Remote Sens. 8 (2008) 2407–2417.

[24] S. Bourennane, C. Fossati, and A. Cailly. Improvement of target-detection algorithms based on adaptive three-dimensional filtering. IEEE Trans. Geosci. Remote Sens. 4 (2010) 1383–1395.

[25] I. Daubechies. Orthonormal bases of compactly supported wavelets. Commun. Pure Appl. Math. 7 (1988) 909–996.

[26] D. Donoho. De-noising by soft-thresholding. IEEE Trans. Inf. Theory 3 (1995) 613–627.

[27] D. Donoho and J. Johnstone. Ideal spatial adaptation by wavelet shrinkage. Biometrika 3 (1994) 425–455.

[28] D. Donoho and I. Johnstone. Adapting to unknown smoothness via wavelet shrinkage. J. Amer. Statist. Assoc. 432 (1995) 1200–1244.

[29] S. Chang, B. Yu, and M. Vetterli. Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. 9 (2000) 1532–1546.
[30] I. Atkinson, F. Kamalabadi, S. Mohan, and D. Jones. Wavelet-based 2-d multichannel signal estimation. Proc. IEEE IGARSS 2 (2003) 743–745.

[31] A. Pizurica and W. Philips. Estimating the probability of the presence of a signal of interest in multiresolution single-and multiband image denoising. IEEE Trans. Image Process. 3 (2006) 654–665.

[32] H. Othman and S. Qian. Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage. IEEE Trans. Geosci. Remote Sens. 2 (2006) 397–408.

[33] A. Cichocki, R. Zdunek, A. Phan, and S. Amari. Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. New Jersey: Wiley, (2009).

[34] R. Coifman and M. Wickerhauser. Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 2 (1992) 713–718.

[35] X. Liu, S. Bourennane, and C. Fossati. Nonwhite noise reduction in hyperspectral images. IEEE Geosci. Remote Sens. Lett. 3 (2012) 368–372.

[36] R. Roger and J. Arnold. Reliably estimating the noise in aviris hyperspectral images. Int. J. Remote Sens. 10 (1996) 1951–1962.

[37] I. C. Chein and D. Qian. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 3 (2004) 608–619.

[38] S. Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 7 (1989) 674–693.

[39] D. Donoho and I. Johnstone. Ideal denoising in an orthonormal basis chosen from a library of bases. Comptes Rendus del’Academie des Sciences-Serie I-Mathematique 12 (1994) 1317–1322.
[40] D. Landgrebe. Multispectral data analysis: A signal theory perspective,” Purdue Univ., West Lafayette, IN (1998).
[41] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol.2 (2011) 1–27:27.

[42] Q. Yuan, L. Zhang, and H. Shen. Hyperspectral image denoising employing a spectral-spatial adaptive total variation model. IEEE Trans. Geosci. Remote Sens. 99 (2012) 1–18.

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