Semi-supervised Remote Sensing Image Segmentation Using Dynamic Region Merging

  • Ning He
  • Ke Lu
  • Yixue Wang
  • Yue Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8008)


This paper introduces a remote sensing image segmentation approach by using semi-supervised and dynamic region merging. In remote sensing images, the spatial relationship among pixels has been shown to be sparsely represented by a linear combination of a few training samples from a structured dictionary. The sparse vector is recovered by solving a sparsity-constrained optimization problem, and it can directly determine the class label of the test sample. Through a graph-based technique, unlabeled samples are actively selected based on the entropy of the corresponding class label. With an initially segmented image based semi-supervised, in which the many regions to be merged for a meaningful segmentation. By taking the region merging as a labeling problem, image segmentation is performed by iteratively merging the regions according to a statistical test. Experiments on two datasets are used to evaluate the performance of the proposed method. Comparisons with the state-of-the-art methods demonstrate that the proposed method can effectively investigate the spatial relationship among pixels and achieve better remote sensing image segmentation results.


Semi-supervised Remote Sensing Image Image segmentation Dynamic region merging 


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  1. 1.
    Bandos, T., Bruzzone, L., Camps-Valls, G.: Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Transaction on Geoscience and Remote Sensing 47(3), 862–873 (2009)CrossRefGoogle Scholar
  2. 2.
    Berge, A., Solberg, A.: Structured gaussian components for hyperspectral image classification. IEEE Transaction on Geoscience and Remote Sensing 44(11), 3386–3396 (2006)CrossRefGoogle Scholar
  3. 3.
    Scholkopf, B., Smola, A.: Learning With KernelsSupport Vector Machines, Regularization, Optimization and Beyond, Cambridge, MA. MIT Press Series (2002)Google Scholar
  4. 4.
    Cawley, G.C., Talbot, N.L.C.: Sparse multinomial logistic regression via bayesian L1 regularisation. In: Advances in Neural Information Processing Systems (2007)Google Scholar
  5. 5.
    Camps-Valls, G., Bruzzone, L.: Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 43, 1351–1362 (2005)CrossRefGoogle Scholar
  6. 6.
    Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R.: Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing 46(11), 3804–3814 (2008)CrossRefGoogle Scholar
  7. 7.
    Plaza, A., Benediktsson, J.A., Boardman, J.W., Brazile, J., Bruzzone, L., Camps-Valls, G., Chanussot, J., Fauvel, M., Gamba, P., Gualtieri, A., Marconcini, M., Tilton, J.C., Trianni, G.: Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment (2009)Google Scholar
  8. 8.
    Camps-Valls, G., Marsheva, T.B., Zhou, D.: Semi-supervised graph-based hyperspectral image classification. IEEE Transaction on Geoscience and Remote Sensing 45(10), 3044–3054 (2007)CrossRefGoogle Scholar
  9. 9.
    Gao, Y., Chua, T.-S.: Hyperspectral image classification by using pixel spatial correlation. In: Li, S., El Saddik, A., Wang, M., Mei, T., Sebe, N., Yan, S., Hong, R., Gurrin, C. (eds.) MMM 2013, Part I. LNCS, vol. 7732, pp. 141–151. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3D object retrieval and recognition with hypergraph analysis. IEEE Transactions on Image Processing 21(9), 4290–4303 (2012)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Marconcini, M., Camps-Valls, G., Bruzzone, L.: A composite semisupervised svm for classification of hyperspectral images. IEEE Geoscience and Remote Sensing Letters 6(2), 234–238 (2009)CrossRefGoogle Scholar
  12. 12.
    Gu, Y., Wang, C., You, D., Zhang, Y., Wang, S., Zhang, Y.: Representative multiple kernel learning for classification in hyperspectral imagery. IEEE Transaction on Geoscience and Remote Sensing 50(7), 2852–2865 (2012)CrossRefGoogle Scholar
  13. 13.
    Park, H.S., Ra, J.B.: Homogeneous region merging approach for image segmentation preserving segmentic object contours. In: Proceedings of the International Workshop on Very Low Bitrate Video Coding, Chicago, IL, pp. 149–152 (1998)Google Scholar
  14. 14.
    Fablet, R., Boucher, J.-M., Augustin, J.-M.: Region-based image segmentation using texture statistics and level-set methods. In: Acoustics, Speech and Signal Processing (2006)Google Scholar
  15. 15.
    Nock, R., Nielsen, F.: Statistical region merging. IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11), 1452–1458 (2004)CrossRefGoogle Scholar
  16. 16.
    Cevahir, C., Aydin Alatan, A.: Region-based image segmentation via graph cuts. In: 15th IEEE International Conference on Image Processing, pp. 2272–2275 (2008)Google Scholar
  17. 17.
    Li, J., Bioucas-Dias, J.M.: Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning. IEEE Transactions on Geoscience and Remote sensing 48(11), 4085–4098 (2010)Google Scholar
  18. 18.
    Kulis, B., Dhillon, S.B.I., Mooney, R.: Semi-supervised graph clustering: a kernel approach. In: Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany (2005)Google Scholar
  19. 19.
    Li, J., Bioucas-dias, J.M., Plaza, A.: Hyperspectral image segmentation using a new bayesian approach with active learning. IEEE Transaction on Geoscience and Remote Sensing 49(10), 3947–3960 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ning He
    • 1
  • Ke Lu
    • 2
  • Yixue Wang
    • 3
  • Yue Gao
    • 4
  1. 1.Beijing Key Laboratory of Information Service EngineeringBeijing Union UniversityBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Shenyang Institute of EngineeringShenyangChina
  4. 4.School of ComputingNational University of SingaporeSingapore

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