Real-time segmentation of remote sensing images with a combination of clustering and Bayesian approaches

Abstract

In the area of remote sensing image processing, accurate segmentation of high-resolution remote sensing images in real time remains a challenging problem and numerous approaches have been developed for the problem. This paper proposes a new unsupervised approach that can efficiently analyze a remote sensing image and provide accurate segmentation results. The approach performs segmentation in three stages. In the first stage, an image is partitioned into blocks of equal sizes. The mean values of the R, G and B components of the pixels in each block are computed to form a feature vector of the block. A preliminary segmentation result is obtained by clustering the feature vectors with a simple clustering algorithm. In the second stage, a Bayesian approach is applied to refine the preliminary segmentation result. In the final stage, a graph-based method is utilized to recognize regions with complex texture structures. The performance of this approach has been tested on a few benchmark datasets, and its segmentation accuracy is compared with that of many state-of-the-art segmentation tools for remote sensing images. The testing results show that the overall segmentation accuracy of the proposed approach is higher than that of the other tools, and real-time analysis suggests that the approach is promising for real-time applications. An implementation of the approach in MATLAB is freely available upon request.

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Availability of data

The source code and testing data of this work are freely available upon request.

References

  1. 1.

    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65(1), 2–16 (2010)

    Article  Google Scholar 

  2. 2.

    Martha, T.R., Kerle, N., van Westen, C.J., Jetten, V., Kumar, K.V.: Segment optimization and data-driven thresholding for knowledge based landslide detection by object-based image analysis. IEEE Trans. Geosci. Remote Sens. 49(12), 4928–4943 (2011)

    Article  Google Scholar 

  3. 3.

    Heumann, B.W.: An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sens. 3(12), 2440–2460 (2011)

    Article  Google Scholar 

  4. 4.

    Li, P., Guo, J., Song, B., Xiao, X.: A multilevel hierarchical image segmentation method for urban impervious surface mapping using very high resolution imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 4(1), 103–116 (2011)

    Article  Google Scholar 

  5. 5.

    dos Santos, J.A., Gosselin, P.-H., Philipp-Foliguet, S., Torres, R.S., Falcão, A.X.: Multiscale classification of remote sensing images. IEEE Trans. Geosci. Remote Sens. 50(10), 3764–3775 (2012)

    Article  Google Scholar 

  6. 6.

    Kurtz, C., Passat, N., Gançarski, P., Puissant, A.: Extraction of complex patterns from multiresolution remote sensing images: a hierarchical top-down methodology. Pattern Recognit. 45(2), 685–706 (2012)

    Article  Google Scholar 

  7. 7.

    Yi, L., Zhang, G., Wu, Z.: A scale-synthesis method for high spatial resolution remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 50(10), 4062–4070 (2012)

    Article  Google Scholar 

  8. 8.

    Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture features. IEEE Trans. Geosci. Remote Sens. 52(1), 16–24 (2014)

    Article  Google Scholar 

  9. 9.

    Shi, X., Li, Y., Zhao, Q.: Flexible hierarchical gaussian mixture model for high-resolution remote sensing image segmentation based on global spatial information. Remote Sens. 12, 1219 (2020)

    Article  Google Scholar 

  10. 10.

    Li, M., Xu, L., Gao, S., Xu, N., Yan, B.: Adaptive segmentation of remote sensing images. Sensors 19, 2385 (2019)

    Article  Google Scholar 

  11. 11.

    Dabboor, M., Collins, M., Karathanassi, V., Braun, A.: An unsupervised classification approach for polarimetric SAR data based on the Chernoff distance for the complex Wishart distribution. IEEE Trans. Geosci. Remote Sens. 51, 4200–4213 (2013)

    Article  Google Scholar 

  12. 12.

    Arii, M., van Zyl, J.J., Kim, Y.: Adaptive model-based decomposition of polarimetric SAR covariance matrices. IEEE Trans. Geosci. Remote Sens. 49, 1104–1113 (2011)

    Article  Google Scholar 

  13. 13.

    Song, W., Li, M., Zhang, P., Wu, Y., Tan, X.: An, L. Mixture WGG-MRF model for PolSAR image classification. IEEE Trans. Geosci. Remote Sens. 56, 905–920 (2018)

    Article  Google Scholar 

  14. 14.

    Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F., Fraundorfer, F.: Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Trans. Geosci. Remote Sens. 5, 8–36 (2017)

    Article  Google Scholar 

  15. 15.

    Volpi, M., Tuia, D.: Dense semantic labeling of subdecimeter resolution images with convolutional neural networks. IEEE Trans. Geosci. Remote Sens. 55, 881–893 (2017)

    Article  Google Scholar 

  16. 16.

    De, S., Bruzzone, L., Bhattacharya, A., Bovolo, F., Chaudhuri, S.: A novel technique based on deep learning and a synthetic target database for classification of urban areas in polSAR data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 154–170 (2018)

    Article  Google Scholar 

  17. 17.

    Wang, Y., He, C., Liu, X., Liao, M.: A hierarchical fully convolutional network integrated with sparse and low-rank subspace representations for polSAR imagery classification. Remote Sens. 10, 342 (2018)

    Article  Google Scholar 

  18. 18.

    Li, Y., Chen, Y., Liu, G., Jiao, L.: A novel deep fully convolutional network for polSAR image classification. Remote Sens. 10, 1984 (2018)

    Article  Google Scholar 

  19. 19.

    Bi, H., Sun, J., Xu, Z.: A graph-based semisupervised deep learning model for polSAR image classification. IEEE Trans. Geosci. Remote Sens. 57, 2116–2132 (2019)

    Article  Google Scholar 

  20. 20.

    Cao, Y., Wu, Y., Zhang, P., Liang, W., Li, M.: Pixel-wise polSAR image classification via a novel complex-valued deep fully convolutional network. Remote Sens. 11, 2653 (2019)

    Article  Google Scholar 

  21. 21.

    Wang, S., Sun, J., Phillips, P., et al.: Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J. Real-Time Image Proc. 15, 631–642 (2018)

    Article  Google Scholar 

  22. 22.

    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)

    Article  Google Scholar 

  23. 23.

    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59, 167–181 (2004)

    Article  Google Scholar 

  24. 24.

    Mardia, K.V., Hainsworth, T.J.: A spatial thresholding method for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 10, 919–927 (1988)

    Article  Google Scholar 

  25. 25.

    Haris, K., Efstratiadis, S.N., Maglaveras, N.: Watershed-based image segmentation with fast region merging. In: Proceedings of the 1998 International Conference on Image Processing, Chicago

  26. 26.

    Myint, S., Gober, P., Brazel, A., Grossman-Clarke, S., Weng, Q.: Perpixel versus object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens. Environ. 115, 1145–1161 (2011)

    Article  Google Scholar 

  27. 27.

    Heumann, B.W.: An object-based classification of mangroves using a hybrid decision tree—support vector machine approach. Remote Sens. 3, 2440–2460 (2011)

    Article  Google Scholar 

  28. 28.

    Baatz, M., Schape, A.: Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: Strobl, J., Blaschke, T., Griesbner, G. (eds.) Angewandte Geographische Informations-Verarbeitung, vol. XII, pp. 12–23. Wichmann Verlag, Karlsruhe (2000)

    Google Scholar 

  29. 29.

    Fu, G., Zhao, H., Li, C., Shi, L.: Segmentation for high-resolution optical remote sensing imagery using improved quadtree and region adjacency graph technique. Remote Sens. 5, 3259–3279 (2013)

    Article  Google Scholar 

  30. 30.

    Banerjee, B., Varma, S., Buddhiraju, K., Eeti, L.: Unsupervised multi-spectral satellite image segmentation combining modified mean-shift and a new minimum spanning tree based clustering technique. IEEE J. Sel. Top. Appl. Top. Earth Obs. Remote Sens. 7, 888–894 (2014)

    Article  Google Scholar 

  31. 31.

    Beaulieu, J.M., Goldberg, M.: Hierarchy in picture segmentation: a stepwise optimization approach. IEEE Trans. Pattern Anal. Mach. Intell. 11, 150–163 (1989)

    Article  Google Scholar 

  32. 32.

    Eppstein, D.: On nearest-neighbor graphs. Discrete Comput. Geom. 17, 263–282 (1997)

    MathSciNet  MATH  Article  Google Scholar 

  33. 33.

    Trémeau, A., Colantoni, P.: Regions adjacency graph applied to color image segmentation. IEEE Trans. Image Process. 9, 735–744 (2000)

    Article  Google Scholar 

  34. 34.

    Ren, X., Malik, J.: Learning a classification model for segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, Nice, Franc. pp. 10–17

  35. 35.

    Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22, 888–905 (2000)

    Article  Google Scholar 

  36. 36.

    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012)

    Article  Google Scholar 

  37. 37.

    Cheng, M.M., Mitra, N.J., Huang, X., Torr, P.H., Hu, S.: Global contrast based salient region detection. IEEE Trans. Pattern Anal. Mach. Intell. 37, 569–582 (2015)

    Article  Google Scholar 

  38. 38.

    Wang, M., Dong, Z., Cheng, Y., Li, D.: Optimal segmentation of high-resolution remote sensing image by combining superpixels with the minimum spanning tree. IEEE Trans. Geosci. Remote Sens. 56, 228–238 (2018)

    Article  Google Scholar 

  39. 39.

    Csillik, O.: Fast segmentation and classification of very high resolution remote sensing data using SLIC superpixels. Remote Sens. 9, 243 (2017)

    Article  Google Scholar 

  40. 40.

    Gu, H., Han, Y., Yang, Y., Li, H., Liu, Z., Soergel, U., Blaschke, T., Cui, S.: An efficient parallel multi-scale segmentation method for remote sensing imagery. Remote Sens. 10, 590 (2018)

    Article  Google Scholar 

  41. 41.

    Hu, Z., Li, Q., Zou, Q., Wu, G.: A bilevel scale-sets model for hierarchical representation of large remote sensing images. IEEE Trans. Geosci. Remote Sens. 54, 7366–7377 (2016)

    Article  Google Scholar 

  42. 42.

    Sun, G., Hao, Y., Chen, X., Ren, J., Zhang, A., Huang, B., Zhang, Y., Jia, X.: Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor. Remote Sens. 9, 899 (2017)

    Article  Google Scholar 

  43. 43.

    Yan, Y., Ren, J., Sun, G., Zhao, H., Han, J., Li, X., Zhan, J.: Unsupervised image saliency detection with gestalt-laws guided optimization and visual attention based refinement. Pattern Recognit. 79, 65–78 (2018)

    Article  Google Scholar 

  44. 44.

    Hu, Z., Wu, Z., Zhang, Q., Fan, Q., Xu, J.: A spatially-constrained color–texture model for hierarchical VHR image segmentation. IEEE Trans. Geosci. Remote Sens. Lett. 10, 120–124 (2013)

    Article  Google Scholar 

  45. 45.

    Zhong, Y., Gao, R., Zhang, L.: Multiscale and multifeature normalized cut segmentation for high spatial resolution remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 54, 6061–6075 (2016)

    Article  Google Scholar 

  46. 46.

    Fu, Z., Sun, Y., Fan, L., Han, Y.: Multiscale and multifeature segmentation of high-spatial resolution remote sensing images using superpixels with mutual optimal strategy. Remote Sens. 10, 1289 (2018)

    Article  Google Scholar 

  47. 47.

    Mikes, S., Haindl, M.: Benchmarking of remote sensing segmentation methods. IEEE J. Sel. Top. Appl. Top. Earth Obs. Remote Sens. 8(5), 2240–2248 (2015)

    Article  Google Scholar 

  48. 48.

    Rottensteiner, F., Sohn, G., Jung, J., Gerke, M., Baillard, C., Benitez, S., Breitkopf, U.: The ISPRS benchmark on urban object classification and 3D building reconstruction. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 1, 293–298 (2012)

    Article  Google Scholar 

  49. 49.

    Haindl, M., Mikeš, S., Pudil, P.: Unsupervised hierarchical weighted multi-segmenter. In: Benediktsson, J., Kittler, J., Roli, F. (eds.) Lecture Notes in Computer Science, vol. 5519, pp. 272–282. Springer, New York (2009)

    Google Scholar 

  50. 50.

    Scarpa, G., Masi, G., Gaetano, R., Verdoliva, L., Poggi, G.: Dynamic hierarchical segmentation of remote sensing images. In: Petrosino, A. (ed.) Image Analysis and Processing, vol. 8156, pp. 371–380. Springer, New York (2013)

    Google Scholar 

  51. 51.

    ENVI/M. https://www.harrisgeospatial.com/Software-Technology/ENVI

  52. 52.

    Haindl, M., Mikeš, S., Vácha, P.: Illumination invariant unsupervised segmenter. In: Proceedings of the IEEE 16th International Conference on Image Processing (ICIP’09), pp. 4025–4028 (2009)

  53. 53.

    R. Gaetano, G. Scarpa, and G. Poggi, “Recursive texture fragmentation and reconstruction segmentation algorithm applied to VHR images,” in Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS’09), vol. 4, 2009, pp. IV–101–IV–104

  54. 54.

    G. Scarpa and M. Haindl, “Unsupervised texture segmentation by spectral-spatial-independent clustering,” in Proc. Int. Conf. Pattern Recogn., 2006, pp. 151–154

  55. 55.

    D’Elia, C., Poggi, G., Scarpa, G.: A tree-structured Markov random field model for Bayesian image segmentation. IEEE Trans. Image Process. 12(10), 1259–1273 (2003)

    MathSciNet  MATH  Article  Google Scholar 

  56. 56.

    Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I., Heynen, M.: Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 58, 239–258 (2004)

    Article  Google Scholar 

  57. 57.

    Reich, B.J., Ghosh, S.K.: Bayesian Statistical Methods, 1st edn. Chapman and Hall, London (2019)

    Google Scholar 

  58. 58.

    Barber, D.: Bayesian Reasoning and Machine Learning, 1st edn. Cambridge University Press, London (2012)

    Google Scholar 

  59. 59.

    Theodoridis, S.: Machine Learning, A Bayesian and Optimization Perspective. Elsevier, Amsterdam (2020)

    Google Scholar 

  60. 60.

    Fieguth, P.: Statistical Image Processing and Multidimensional Modeling. Springer, New York (2012)

    Google Scholar 

  61. 61.

    Liu, Y., Bian, L., Meng, Y., Wang, H., Zhang, S., Yang, Y.: Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS J. Photogramm. Remote Sens. 68, 144–156 (2012)

    Article  Google Scholar 

  62. 62.

    Polak, M., Zhang, H., Pi, M.: An evaluation metric for image segmentation of multiple objects. Image Vis. Comput. 27, 1223–1227 (2009)

    Article  Google Scholar 

  63. 63.

    Cheng, G., Cheng, J., Luo, M., et al.: Effective and efficient multitask learning for brain tumor segmentation. J. Real-Time Image Proc. (2020). https://doi.org/10.1007/s11554-020-00961-4

    Article  Google Scholar 

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Acknowledgements

The authors are grateful for the constructive comments and suggestions from the anonymous reviewers on an earlier version of the paper.

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Correspondence to Yinglei Song.

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Song, Y., Qu, J. Real-time segmentation of remote sensing images with a combination of clustering and Bayesian approaches. J Real-Time Image Proc (2020). https://doi.org/10.1007/s11554-020-00990-z

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Keywords

  • Remote sensing images
  • Segmentation
  • Clustering
  • Bayesian approach