A Graph-Based Image Segmentation Approach for Image Classification and Its Application on SAR Images

• Autorzy: Sharifi M. , Kiani K. , Kheirkhahan M.

Warianty tytułu

PL

Segmentacja metodą grafową w klasyfikacji obrazów w zastosowaniu do obrazów SAR

• Języki publikacji: EN

Abstrakty

EN

In this paper, we propose a novel approach for image classification based on Graph-based image segmentation method and apply it on SAR images with satisfactory clustering performance and low computational cost. In this method first, the image pre-processes by mean shift algorithm to cluster into disjoint region, then the segmented regions are represented as a graph structure with all connected neighbourhood, and after that normalized cut method is applied to classify image into defined classes.

W artykule przedstawiono metodę klasyfikacji obrazów, z wykorzystaniem segmentacji metodą grafową. Proponowana rozwiązanie wykorzystano w analizie obrazów SAR.

Słowa kluczowe

EN

polarimetric radar; aperture; SAR image analysis; graph partitioning; normalized cut

PL

radar polarymetryczny; apertura numeryczna; obraz SAR; podział grafu; cięcie

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 4
• Strony: 202--205
• Opis fizyczny: Bibliogr. 42 poz., rys.

Twórcy

autor

Sharifi M.

• Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

autor

Kiani K.

• Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

autor

Kheirkhahan M.

• Department of Electrical and Computer Engineering, Semnan University, Semnan, Iran

Bibliografia

• [1] Yanqiu C., ZHANG T., XU S., YU W., Image Despeckling Based on LMMSE Wavelet Shrinkage, Przegląd Elektrotechniczny, 88 (2012), nr 7b, 269-272
• [2] Lee JS., Grunes MR., Ainsworth T., Du L-J., Schuler D., Cloude SR., Unsupervised classification using polarimetric decomposition and the complex Wishart classifier, IEEE Trans Geosci Remote Sens, 37(1999), No. 5, 2249–57
• [3] Liu G., Xiong H., Huang S., Study on segmentation and interpretation of multilook polarimetric SAR images, Int J Remote Sens, 21(2000), NO. 8, 1675–91
• [4] Lovberg M., Krink T., Extending particle swarm optimisers with self-organized criticality, IProc of the IEEE congress on evolutionary computation, 2(2002),1588–93
• [5] Omran M., Salman A., Engelbrecht AP., Image classification using particle swarm optimization, Conf on simulated evolution and learning, 1(2002), 370–4
• [6] Omran MG., Salman A., Engelbrecht AP., Dynamic clustering using particle swarm optimization with application in image segmentation, Pattern Anal Appl, 8(2006), 332–44
• [7] Pal NR., Biswas J., Cluster validation using graph theoretic concepts, Pattern Recogn (1997), 847–57
• [8] Pottier E., Lee JS., Unsupervised classification scheme of POLSAR images basedon the complex Wishart distribution and the H/A/alpha-Polarimetric decomposition theorem, Proc of the 3rd EUSAR 2000 conf, May 2000
• [9] Pottier E., Lee JS., Ferro-Famil L., Advanced concepts in polarimetry – part 2(polarimetric target classification), Technical report, NATO RTO-EN-SET-081, (2005) no. 5, 1–38
• [10] Shi Y., Eberhart RC., A modified particle swarm optimizer, Proc of the IEEE congress on evolutionary computation, (1998), 69–73
• [11] Riget J., Vesterstrom JS., A diversity-guided particle swarm optimizer – the ARPSO, Technical report, Department of Computer Science, University of Aarhus, (2002)
• [12] Rignot E., Chellappa R., Dubois P., Unsupervised segmentation of polarimetric SAR data using the covariance matrix, IEEE Trans Geosci Remote Sens, 30(1992), (July), 697–705
• [13] Tan CP., Lim KS., Ewe HT., Image processing in polarimetric SAR images using a hybrid entropy decomposition and maximum likelihood (EDML), Proc int symposium on image and signal processing and analysis (ISPA), September 2007, 418–22
• [14] Tran TN., Wehrens R., Hoekman DH., Buydens LMC., Initialization of Markov random field clustering of large remote sensing images, IEEE Trans Geosci Remote Sens, 43(2005), No. 8, 1912–9
• [15] Turi RH., Clustering-based colour image segmentation. Ph.D. Thesis, Monash University, Australia, (2001).
• [16] US Geological Survey Images <http://terraserver-usa.com>
• [17] Van den Bergh F., An analysis of particle swarm optimizers. Ph.D. Thesis, Department of Computer Science, University of Pretoria, Pretoria, South Africa; (2002)
• [18] Van den Bergh F., Engelbrecht AP., A new locally convergent particle swarm optimizer, Proc of the IEEE international conference on systems, man, and cybernetics, (2002), 96–101
• [19] Wu Y., Ji K., Yu W., Su Y., Region-based classification of polarimetric SAR images using Wishart MRF, IEEE Geosci Rem Sens Lett, 5(2008), No. 4, 668–72
• [20] Zhen Y., Cheng-Chang L., Wavelet-based unsupervised SAR image segmentation using hidden markov tree models, Proc of the 16th international conference on pattern recognition (ICPR’02), 2(2002), 20729
• [21] Zhang B., Hsu M., K-Harmonic means – a data clustering algorithm, Hewlett-Packard Labs Technical Report HPL, 124 (1999)
• [22] Zhang L., Zhang J., Zou B, Zhang Y., Comparison of methods for target detection and applications using polarimetric SAR image, PIERS Online, 4(2008), No. 1, 140–5
• [23] van Zyl JJ., Unsupervised classification of scattering mechanisms using radar polarimetry data, IEEE Trans Geosci Remote Sens, 27(1989), (January), 36–45
• [24] Akbari V., Moser G., Doulgeris A.P., Anfinsen S.N., Eltoft T., Serpico S.B., A K-Wishart Markov random field model for clustering of polarimetric SAR imagery, Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, (2011), 1357-1360
• [25] Yin J.J., Yang J., Yamaguchi Y., A new method for polarimetric SAR image classification, 2nd Asian-Pacific Conference on Synthetic Aperture Radar, Xian, China Oct. (2009), 733-737
• [26] Zhang D., Wu L.-N., Wei G., A new classifier for polarimetric SAR images. Progress In Electromagnetics Research, PIER 94(2009), 83-104
• [27] Uhlmann S., Kiranyaz S., Gabbouj M., Ince T., Polarimetric SAR images classification using collective network of binary classifiers, Urban Remote Sensing Event (JURSE), Joint (2011), 245–248, Digital Object Identifier: 10.1109/JURSE.2011.5764765
• [28] Comaniciu D., Meer P., Mean shift: A robust approach toward feature space analysis, IEEE Trans. Pattern Anal. Mach. Intell., 24(2002), No. 5, 603–619
• [29] Cheng Y., Mean shift, mode seeking, and clustering, IEEE Trans. Pattern Anal. Mach. Intell., 17(1995), No. 8, 790–799.
• [30] Comaniciu D., An algorithm for data-driven bandwidth selection, IEEE Trans. Pattern Anal. Mach. Intell., 25(2003), No. 2, 281–288
• [31] Costeira J., Kanade T., A multibody factorization method for motion analysis, Int. Conf. Comput. Vis., (1995), 1071–1076
• [32] Wang S., Siskind J. M., Image segmentation with ratio cut, IEEE Trans. Pattern Anal. Mach. Intell., 25(2003), No. 6, 675–690
• [33] Shi J., Malik J., Normalized cuts and image segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 22(2000), No. 8, 888–905
• [34] Weiss Y., Segmentation using eigenvectors: A unifying view, Int. Conf. Comput. Vis., (1999), 957–982
• [35] Jermyn I. H., Ishikawa H., Globally optimal regions and boundaries as minimum ratio cycles, IEEE Trans. Pattern Anal. Mach. Intell., 23(2001), No. 10, 1075–1088
• [36] Chen Y., Wang J. Z., Krovetz R., CLUE: Cluster-based retrieval of images by unsupervised learning, IEEE Trans. Image Process., 14(2005), No. 8, 1187–1201
• [37] Ngo C.-W., Ma Y.-F., Zhang H.-J., Video summarization and scene detection by graph modeling, IEEE Trans. Circuits Syst. Video Technol.,15(2005), No. 2, 296–305
• [38] Sarkar S., Soundararajan P., Supervised learning of large perceptual organization: Graph spectral partitioning and learning automata, IEEE Trans. Pattern Anal. Mach. Intell., 22(2000), No. 5, 504–525
• [39] Yu S. X., Shi J., Multiclass spectral clustering, Proc. Int. Conf.Comput. Vis., (2003), 313–319
• [40] Wu Z.-Y., Leahy R., An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation, IEEE Trans.Pattern Anal. Mach. Intell., 15(1993), No. 11, 1101–1113
• [41] Ding J., Image segmentation using Normalized Cut and Mean Shift, December 3, (2007), homepage.usask.ca
• [42] Tao W., Jin H., Zhang Y., Color Image Segmentation Based on Mean Shift and Normalized Cuts, IEEE Transactions On Systems, Man, and Cybernetics—Part B: Cybernetics, 37(2007), No. 5, 1382-1389