This paper introduces a non-linear Polarimetric SAR data filtering approach able to preserve the edges and small details of the data. It is based on exploiting the data locality in both, the spatial and the polarimetric domains, in order to avoid mixing heterogeneous samples of the data. A weighted average is performed over a given window favoring pixel values that are close on both domains. The filtering technique is based on a modified bilateral filtering, which is defined in terms of spatial and polarimetric distances. These distances encapsulate all the knowledge in both domains for an adaptation to the data structure. Finally, the proposed technique is employed to process a real RADARSAT-2 dataset.
References
[1]
Kostinski, A.; Boerner, W. On foundations of radar polarimetry. IEEE Trans. Antennas Propag 1986, 34, 1395–1404.
[2]
Lee, J.; Pottier, E. Polarimetric Radar Imaging: From Basics to Applications; CRC Press: Boca Raton, FL, USA, 2009.
[3]
Cloude, S. Polarisation: Applications in Remote Sensing; Oxford University Press: Cary, NC, USA, 2009.
[4]
Elachi, C.; van Zyl, J. Introduction To The Physics and Techniques of Remote Sensing; John Wiley & Sons: Hoboken, NJ, USA, 2006.
[5]
Ulaby, F.; Elachi, C. Radar Polarimetry for Geoscience Applications; Artech House Remote Sensing Library, Artech House: London, UK, 1990.
[6]
Ouchi, K. Recent trend and advance of synthetic aperture radar with selected topics. Remote Sens 2013, 5, 716–807.
[7]
Lee, J. Refined filtering of image noise using local statistics. Comput. Graph. Image Process 1981, 15, 380–389.
[8]
Vasile, G.; Trouve, E.; Lee, J.S.; Buzuloiu, V. Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Trans. Geosci. Remote Sens 2006, 44, 1609–1621.
[9]
Tomasi, C.; Manduchi, R. Bilateral Filtering for Gray and Color Images. Proceedings of the Sixth International Computer Vision Conference, Bombay, India, 4–7 January 1998; pp. 839–846.
[10]
Buades, A.; Coll, B.; Morel, J.M. A Non-Local Algorithm for Image Denoising. Proceedings of the IEEE Computer Society Conference Computer Vision and Pattern Recognition CVPR 2005, San Diego, CA, USA, 20–25 June 2005; 2, pp. 60–65.
[11]
Zhang, W.; Liu, F.; Jiao, L. SAR image despeckling via bilateral filtering. Electron. Lett 2009, 45, 781–783.
[12]
Deledalle, C.A.; Tupin, F.; Denis, L. Polarimetric SAR Estimation Based on Non-Local Means. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, USA, 25–30 July 2010; pp. 2515–2518.
[13]
Maleki, A.; Narayan, M.; Baraniuk, R. Suboptimality of nonlocal means for images with sharp edges. Appl. Comput. Harmon. Anal 2012, 33, 370–387.
[14]
Kersten, P.R.; Lee, J.S.; Ainsworth, T.L. Unsupervised classification of polarimetric synthetic aperture radar images using fuzzy clustering and EM clustering. IEEE Trans. Geosci. Remote Sens 2005, 43, 519–527.
[15]
Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P. Filtering and segmentation of polarimetric SAR data based on binary partition trees. IEEE Trans. Geosci. Remote Sens 2012, 50, 593–605.
[16]
Cloude, S.R.; Pottier, E. A review of target decomposition theorems in radar polarimetry. IEEE Trans. Geosci. Remote Sens 1996, 34, 498–518.
[17]
Goodman, J.W. Some fundamental properties of speckle. J. Opt. Soc. Am 1976, 66, 1145–1150.
[18]
Goodman, N. Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction). Ann. Math. Stat 1963, 34, 152–177.
[19]
Tough, R.; Blacknell, D.; Quegan, S. A statistical description of polarimetric and interferometric synthetic aperture radar data. Proc. R. Soc. Lond. Series A: Math. Phys. Sci 1995, 449, 567–589.
[20]
Lee, J.S.; Hoppel, K.W.; Mango, S.A.; Miller, A.R. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery. IEEE Trans. Geosci. Remote Sens 1994, 32, 1017–1028.
[21]
Alonso-González, A.; Valero, S.; Chanussot, J.; López-Martínez, C.; Salembier, P. Processing multidimensional SAR and hyperspectral images with binary partition tree. Proc. IEEE 2012, 1–25.
[22]
Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P. Variable Local Weight Filtering for PolSAR Data Speckle Noise Reduction. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany, 22–27 July 2012; pp. 2133–2136.
[23]
Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P. PolSAR Speckle Filtering and Segmentation Based on Binary Partition Tree Representation. Proceedings of the ESA PolInSAR, Frascati, Italy, 24–28 January 2011.
[24]
Barbaresco, F. Interactions between Symmetric Cone and Information Geometries: Bruhat-Tits and Siegel Spaces Models for High Resolution Autoregressive Doppler Imagery. In Emerging Trends in Visual Computing; Nielsen, F., Ed.; Springer: Berlin/Heidelberg, Germany, 2009; Volume 5416, pp. 124–163.
[25]
Freeman, A. SAR calibration: An overview. IEEE Trans. Geosci. Remote Sens 1992, 30, 1107–1121.
[26]
Eisemann, E.; Durand, F. Flash photography enhancement via intrinsic relighting. ACM Trans. Graph 2004, 23, 673–678.
[27]
Google Earth. Available online: http://earth.google.com (accessed on 15 October 2013).
[28]
PolSARPro v. 4.0.3. Available online: http://earth.esa.int/polsarpro (accessed on 4 February 2010).
[29]
Anfinsen, S.; Doulgeris, A.; Eltoft, T. Estimation of the equivalent number of looks in polarimetric synthetic aperture radar imagery. IEEE Trans. Geosci. Remote Sens 2009, 47, 3795–3809.
[30]
Oliver, C.; Quegan, S. Understanding Synthetic Aperture Radar Images; SciTech Publishing: Raleigh, NC, USA, 2004.
[31]
Salembier, P.; Garrido, L. Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. Image Process 2000, 9, 561–576.
[32]
Alonso-Gonzalez, A.; Lopez-Martinez, C.; Salembier, P. Filtering and Segmentation of Polarimetric SAR Images with Binary Partition Trees. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI USA, 25–30 July 2010; pp. 4043–4046.