Abstract
This paper investigates several pruning techniques applied on Binary Partition Trees (BPTs) and their usefulness for low-level processing of PolSAR images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph-cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Initial experiments are also reported on the use of Minkowski norms in the definition of the optimization criterion.
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References
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(11), 2274–2282 (2012)
Alonso-Gonzalez, A., Lopez-Martinez, C., Salembier, P.: Filtering and segmentation of polarimetric SAR data based on binary partition trees. IEEE Trans. on Geoscience and Remote Sensing 50(2), 593–605 (2012)
Alonso-Gonzalez, A., Valero, S., Chanussot, J., Lopez-Martinez, C., Salembier, P.: Processing multidimensional SAR and hyperspectral images with binary partition tree. Proceedings of IEEE 101(3), 723–747 (2013)
Barbaresco, F.: Interactions between symmetric cone and information geometries: Bruhat-tits and siegel spaces models for high resolution autoregressive doppler imagery. In: Nielsen, F. (ed.) ETVC 2008. LNCS, vol. 5416, pp. 124–163. Springer, Heidelberg (2009)
Cloude, S.: Polarisation Applications in Remote Sensing. Oxford Univ. Press (2009)
Cloude, S., Pottier, E.: A review of target decomposition theorems in radar polarimetry. IEEE Trans. on Geosc. and Remote Sens. 34(2), 498–518 (1996)
Deledalle, C.A., Tupin, F., Denis, L.: Polarimetric SAR estimation based on non-local means. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2010 (2010)
Foucher, S., Lopez-Martinez, C.: Analysis, evaluation, and comparison of polarimetric SAR speckle filtering techniques. IEEE Trans. on Image Processing 23(4), 1751–1764 (2014)
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. on Geoscience and Remote Sensing 43(3), 519–527 (2005)
Kiran, B.R.: Energetic-Lattice based optimization. PhD thesis, Université Paris-Est (2014)
Lee, J.-S., Ainsworth, T.L., Kelly, J.P., López-MartÃnez, C.: Evaluation and bias removal of multilook effect on entropy/alpha/anisotropy in polarimetric SAR decomposition. IEEE Trans. on Geosc. and Remote Sens. 46(10), 3039–3051 (2008)
Lee, J.-S., Hoppel, K., Mango, S., Miller, A.: Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery. IEEE Trans. on Geoscience and Remote Sensing 32(5), 1017–1028 (1994)
Lee, J.-S., Pottier, E.: Polarimetric Radar Imaging: From Basics to Applications. CRC Press, Boca Raton (2009)
Lee, J.-S., Wen, J.H., Ainsworth, T.L., Chen, K.S., Chen, A.J.: Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans. on Geoscience and Remote Sens. 47(1), 202–213 (2009)
Martin, D., Fowlkes, C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(5), 530–549 (2004)
Pont-Tuset, J., Marques, F.: Measures and meta-measures for the supervised evaluation of image segmentation. In: Computer Vision and Pattern Recognition (CVPR) (2013)
Salembier, P., Foucher, S., Lopez-Martinez, C.: Low-level processing of PolSAR images with binary partition trees. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2014, Quebec, Canada (July 2014)
Salembier, P., Garrido, L.: Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval. IEEE Trans. on Image Processing 9(4), 561–576 (2000)
Serra, J.: Hierarchies and optima. In: Debled-Rennesson, I., Domenjoud, E., Kerautret, B., Even, P. (eds.) DGCI 2011. LNCS, vol. 6607, pp. 35–46. Springer, Heidelberg (2011)
Valero, S., Salembier, P., Chanussot, J.: Hyperspectral image representation and processing with binary partition trees. IEEE Trans. on Image Processing 22(4), 1430–1443 (2013)
Veganzones, M.A., Tochon, G., Dalla-Mura, M., Plaza, A.J., Chanussot, J.: Hyperspectral image segmentation using a new spectral unmixing-based binary partition tree representation. IEEE Trans. on Image Proc. 23(8), 3574–3589 (2014)
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Salembier, P. (2015). Study of Binary Partition Tree Pruning Techniques for Polarimetric SAR Images. In: Benediktsson, J., Chanussot, J., Najman, L., Talbot, H. (eds) Mathematical Morphology and Its Applications to Signal and Image Processing. ISMM 2015. Lecture Notes in Computer Science(), vol 9082. Springer, Cham. https://doi.org/10.1007/978-3-319-18720-4_5
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DOI: https://doi.org/10.1007/978-3-319-18720-4_5
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