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SAR image segmentation with parallel region merging

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Abstract

In this paper, a parallel region merging strategy is proposed for partitioning Synthetic Aperture Radar (SAR) images into several disjoint regions, based on the region adjacency graph (RAG) of an initial partition and the nearest neighbor graph (NNG) produced from the RAG. Developed from the multi-direction ratio edge detector, a multi-scale-multi-direction (MSMD) one is used to extract edge strength map (ESM) of an initial SAR image, feeded into watershed transform to generate an initial partition result of the initial SAR image. Considering local image properties, which makes the generated NNG center around bi-node circles situated in interiors of homogeneous regions, many of which are independently located in different homogeneous regions, the predication of the parallelizability for bi-node circles is proposed to make the proposed parallel region merging strategy. The proposed parallel merging strategy simultaneously merges bi-node circles far away from boundaries of regions, characterized by the length of path from a node to the bi-node circle in the NNG. The performance of the proposed parallel merging strategy is analyzed theoretically and experimentally, and our experiments show that the proposed method outweighs other compared methods.

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References

  1. Akyilmaz E (2018) Multilogit prior-based gamma mixture model for segmentation of sar images. In: IEEE geoscience and remote sensing letters

  2. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intel 33 (5):898–916

    Article  Google Scholar 

  3. Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intel 39(12):2481–2495

    Article  Google Scholar 

  4. Calderero F, Marques F (2010) Region merging techniques using information theory statistical measures. IEEE Trans Image Process 19(6):1567–1586

    Article  MathSciNet  Google Scholar 

  5. Carvalho EA, Ushizima DM, Medeiros FN, Martins CIO, Marques RC, Oliveira IN (2010) Sar imagery segmentation by statistical region growing and hierarchical merging. Digital Signal Process 20(5):1365–1378

    Article  Google Scholar 

  6. Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266– 277

    Article  Google Scholar 

  7. Cook R, McConnell I, Oliver CJ, Welbourne E (1994) Mum (merge using moments) segmentation for sar images. In: SAR Data processing for remote sensing, vol 2316, International Society for Optics and Photonics, pp 92–104

  8. Duan Y, Liu F, Jiao L, Zhao P, Zhang L (2017) Sar image segmentation based on convolutional-wavelet neural network and markov random field. Pattern Recogn 64:255–267

    Article  Google Scholar 

  9. Eppstein D, Paterson MS, Yao FF (1997) On nearest-neighbor graphs. Discrete & Computational Geometry 17(3):263–282

    Article  MathSciNet  Google Scholar 

  10. Fan S, Sun Y, Shui P (2020) Region-merging method with texture pattern attention for sar image segmentation. IEEE Geoscience and Remote Sensing Letters

  11. Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181

    Article  Google Scholar 

  12. Fjortoft R, Lopes A, Marthon P, Cubero-Castan E (1998) An optimal multiedge detector for sar image segmentation. IEEE Trans Geosci Remote Sens 36(3):793–802

    Article  Google Scholar 

  13. Galland F, Bertaux N, Réfrégier P (2003) Minimum description length synthetic aperture radar image segmentation. IEEE Trans Image Process 12(9):995–1006

    Article  Google Scholar 

  14. Gao L, Song J, Nie F, Zou F, Sebe N, Shen HT (2016) Graph-without-cut: an ideal graph learning for image segmentation. In: Thirtieth AAAI conference on artificial intelligence

  15. Gao L, Song J, Zhang D, Shen HT (2018) Coarse-to-fine image co-segmentation with intra and inter rank constraints. pp 719–725

  16. Gemme L, Dellepiane SG (2018) An automatic data-driven method for sar image segmentation in sea surface analysis. IEEE Trans Geosci Remote Sens 56 (5):2633–2646

    Google Scholar 

  17. Grunwald PD (2007) The minimum description length principle. MIT Press

  18. Haris K, Efstratiadis SN, Maglaveras N (1998) Watershed-based image segmentation with fast region merging. In: International conference on image processing, IEEE, pp 338–342

  19. Komodakis N, Xiang B, Paragios N (2015) A framework for efficient structured max-margin learning of high-order mrf models. IEEE Trans Pattern Anal Mach Intel 37(7):1425–1441

    Article  Google Scholar 

  20. Lang F, Yang J, Li D, Zhao L, Shi L (2014) Polarimetric sar image segmentation using statistical region merging. IEEE Geosci Remote Sens Lett 11(2):509–513

    Article  Google Scholar 

  21. Lei P, Zheng T, Wang J, Bai X (2020) A joint convolutional neural network for simultaneous despeckling and classification of sar targets. IEEE Geoscience and Remote Sensing Letters

  22. Li BN, Qin J, Wang R, Wang M, Li X (2016) Selective level set segmentation using fuzzy region competition. IEEE Access 4:4777–4788

    Article  Google Scholar 

  23. Li SZ (1994) Markov random field models in computer vision. In: European conference on computer vision, Springer, pp 361–370

  24. Li W, Benie GB, He DC, Wang S, Ziou D, Hugh Q, Gwyn J (1999) Watershed-based hierarchical sar image segmentation. Int J Remote Sens 20(17):3377–3390

    Article  Google Scholar 

  25. Liu Z, Li X, Luo P, Loy CC, Tang X (2018) Deep learning markov random field for semantic segmentation. IEEE Trans Pattern Anal Mach Intel 40(8):1814–1828

    Article  Google Scholar 

  26. Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. pp 3431–3440

  27. Lu Y, Chen Y, Zhao D, Liu B, Lai Z, Chen J (2020) Cnn-g: Convolutional neural network combined with graph for image segmentation with theoretical analysis. IEEE Transactions on Cognitive and Developmental Systems

  28. Luo S, Tong L, Chen Y (2018) A multi-region segmentation method for sar images based on the multi-texture model with level sets. IEEE Trans Image Process 27(5):2560–2574

    Article  MathSciNet  Google Scholar 

  29. Marques RCP, Medeiros FN, Nobre JS (2012) Sar image segmentation based on level set approach and \({g_{a}^{0}}\) model. IEEE Trans Pattern Anal Mach Intel 34(10):2046–2057

    Article  Google Scholar 

  30. Nie X, Qiao H, Zhang B (2015) A variational model for polsar data speckle reduction based on the wishart distribution. IEEE Trans Image Process 24(4):1209–1222

    Article  MathSciNet  Google Scholar 

  31. Nock R, Nielsen F (2004) Statistical region merging. IEEE Trans Pattern Anal Mach Intel 26(11):1452–1458

    Article  Google Scholar 

  32. Oliver C, Blacknell D, White R (1996) Optimum edge detection in sar. IEE Proceedings-Radar Sonar and Navigation 143(1):31–40

    Article  Google Scholar 

  33. Pan X, Zhou Y, Li F, Zhang C (2017) Superpixels of rgb-d images for indoor scenes based on weighted geodesic driven metric. IEEE Trans Vis Comput Graph 23(10):2342–2356

    Article  Google Scholar 

  34. Panagiotakis C, Grinias I, Tziritas G (2011) Natural image segmentation based on tree equipartition, bayesian flooding and region merging. IEEE Trans Image Process 20(8):2276–2287

    Article  MathSciNet  Google Scholar 

  35. Paragios N, Deriche R (2002) Geodesic active regions and level set methods for supervised texture segmentation. Int J Comput Vis 46(3):223–247

    Article  Google Scholar 

  36. Peng B, Zhang L, Zhang D (2011) Automatic image segmentation by dynamic region merging. IEEE Trans Image Process 20(12):3592–3605

    Article  MathSciNet  Google Scholar 

  37. Picco M, Palacio G (2011) Unsupervised classification of sar images using markov random fields and \({g_{i}^{0}}\) model. IEEE Geosci Remote Sens Lett 8(2):350–353

    Article  Google Scholar 

  38. Shui PL, Zhang ZJ (2014) Fast sar image segmentation via merging cost with relative common boundary length penalty. IEEE Trans Geosci Remote Sens 52(10):6434–6448

    Article  Google Scholar 

  39. Smits PC, Dellepiane SG (1997) Synthetic aperture radar image segmentation by a detail preserving markov random field approach. IEEE Trans Geosci Remote Sens 35(4):844–857

    Article  Google Scholar 

  40. Touzi R, Lopes A, Bousquet P (1988) A statistical and geometrical edge detector for sar images. IEEE Trans Geosci Remote Sens 26(6):764–773

    Article  Google Scholar 

  41. Wang F, Wu Y, Li M, Zhang P, Zhang Q (2017) Adaptive hybrid conditional random field model for sar image segmentation. IEEE Trans Geosci Remote Sens 55(1):537–550

    Article  Google Scholar 

  42. Xia GS, Liu G, Yang W, Zhang L (2016) Meaningful object segmentation from sar images via a multiscale nonlocal active contour model. IEEE Trans Geosci Remote Sens 54(3):1860–1873

    Article  Google Scholar 

  43. Yang J, Gan Z, Li K, Hou C (2015) Graph-based segmentation for rgb-d data using 3-d geometry enhanced superpixels. IEEE Trans Cybern 45 (5):927–940

    Article  Google Scholar 

  44. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122

  45. Yu H, Zhang X, Wang S, Hou B (2013) Context-based hierarchical unequal merging for sar image segmentation. IEEE Trans Geosci Remote Sens 51 (2):995–1009

    Article  Google Scholar 

  46. Yu Q, Clausi DA (2008) Irgs: Image segmentation using edge penalties and region growing. IEEE Trans Pattern Anal Mach Intel 30(12):2126–2139

    Article  Google Scholar 

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Authors and Affiliations

  1. College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China

    Zejun Zhang

  2. Key Laboratory of Smart Agriculture and Forestry, Fujian Province University, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Xiong Pan & Kai He

  3. The Digital Fujian Institute of Big Data for Agriculture and Forestry, College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Xiong Pan, Kai He, Changcai Yang & Riqing Chen

  4. Jinshan College, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Li Cheng

Authors
  1. Zejun Zhang
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  2. Xiong Pan
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  3. Kai He
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  4. Li Cheng
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  5. Changcai Yang
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  6. Riqing Chen
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Correspondence to Zejun Zhang.

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Zhang, Z., Pan, X., He, K. et al. SAR image segmentation with parallel region merging. Multimed Tools Appl 80, 5701–5721 (2021). https://doi.org/10.1007/s11042-020-09920-4

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  • DOI: https://doi.org/10.1007/s11042-020-09920-4

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