Cluster Computing

, Volume 21, Issue 1, pp 39–49 | Cite as

Automatic change detection using multiindex information map on high-resolution remote sensing images

  • R. KishorekumarEmail author
  • P. Deepa


The problem of high quality training samples and high-dimensional data is encountered in high-resolution image change detection. To address these problems, a novel automatic change detection algorithm in bitemporal multispectral images of the same scene using multiindex information is presented. The conspicuous advantages of the proposed algorithm are: (i) the complicated urban scenes are represented by a set of low-level semantic information index (e.g., textural and structural features), the information indices can directly indicate the primitive urban classes and (ii) change detection is carried out automatically using unsupervised approach. The multiindex information map contains vegetation, water and building extracted using enhanced vegetation index, normalized difference water index and developed efficient morphological building index respectively. The proposed algorithm is validated on the multitemporal Landsat ETM+ images over Coimbatore, Tamilnadu, India where auspicious results were achieved by the proposed method. Moreover, the traditional method based on the pixel based change detection has also been implemented for the purpose of comparison to further validate the advantages of the proposed model.


Change detection Multiindex information map Morphological building index Landsat ETM+ 


  1. 1.
    Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRefGoogle Scholar
  2. 2.
    Hussain, M., Chen, D., Cheng, A., Wei, H., Stanley, D.: Change detection from remotely sensed images: from pixel-based to object-based approaches. ISPRS J. Photogramm. Remote Sens. 80, 91–106 (2013)CrossRefGoogle Scholar
  3. 3.
    Yoon, S.C., Shin, T.S., Lawrence, K.C., Heitschmidt, G.W., Park, B., Gamble, G.R.: Hyperspectral imaging using RGB color for food borne pathogen detection. J. Electron. Imaging 24(4), 043008 (2015)CrossRefGoogle Scholar
  4. 4.
    Zhao, R., Wang, Q., Shen, Y.: Kronecker compressive sensing-based mechanism with fully independent sampling dimensions for hyperspectral imaging. J. Electron. Imaging 24(6), 063012 (2015)CrossRefGoogle Scholar
  5. 5.
    Song, X., He, G., Zhang, Z., Long, T., Peng, Y., Wang, Z.: Visual attention model based mining area recognition on massive high-resolution remote sensing images. Cluster Comput. 18(2), 541–548 (2015)CrossRefGoogle Scholar
  6. 6.
    Chen, L., Ma, Y., Liu, P., Wei, J., Jie, W., He, J.: A review of parallel computing for large-scale remote sensing image mosaicking. Cluster Comput. 18(2), 517–529 (2015)CrossRefGoogle Scholar
  7. 7.
    Brook, A.: Three-dimensional wavelets-based denoising of hyperspectral imagery. J. Electron. Imaging 24(1), 013034 (2015)CrossRefGoogle Scholar
  8. 8.
    Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS J. Photogramm. Remote Sens. 58(3/4), 225–238 (2004)CrossRefGoogle Scholar
  9. 9.
    Celik, T., Ma, K.-K.: Multitemporal image change detection using undecimated discrete wavelet transform and active contours. IEEE Trans. Geosci. Remote Sens. 49(2), 706–716 (2011)CrossRefGoogle Scholar
  10. 10.
    Celik, T.: Multi scale change detection in multitemporal satellite images. IEEE Geosci. Remote Sens. Lett. 6(4), 820–824 (2009)CrossRefGoogle Scholar
  11. 11.
    Suresh, A., Shunmuganathan, K.L.: Feature fusion technique for colour texture classification system based on gray level co-occurrence matrix. J. Comput. Sci. 8(12), 2103–2111 (2012)CrossRefGoogle Scholar
  12. 12.
    Plaza, J., Pérez, R., Plaza, A., Martinez, P., Valencia, D.: Parallel morphological/neural processing of hyperspectral images using heterogeneous and homogeneous platforms. Cluster Comput. 11(1), 17–32 (2008)CrossRefGoogle Scholar
  13. 13.
    Volpi, M., Tuia, D., Bovolo, F., Kanevski, M., Bruzzone, L.: Supervised change detection in VHR images using contextual information and support vector machines. Int. J. Appl. Earth Observ. Geoinf. 20, 77–85 (2013)CrossRefGoogle Scholar
  14. 14.
    Lei, Z., Fang, T., Huo, H., Li, D.: Bi-temporal texton forest for land cover transition detection on remotely sensed imagery. IEEE Trans. Geosci. Remote Sens. 52(2), 1227–1237 (2014)CrossRefGoogle Scholar
  15. 15.
    Wang, L., Song, W., Liu, P.: Link the remote sensing big data to the image features via wavelet transformation. Cluster Comput. 19(2), 793–810 (2016)CrossRefGoogle Scholar
  16. 16.
    Suresh, A., Shunmuganathan, K.L.: A novel colour texture classification approach based on gray level co-occurrence matrix. Int. J. Comput. Inf. Syst. 5(3), 71–75 (2012)Google Scholar
  17. 17.
    Camps-Valls, G., Gomez-Chova, L., Muñoz-Mari, J., Rojo-Alvarez, J.L., Martinez-Ramon, M.: Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Trans. Geosci. Remote Sens. 46(6), 1822–1835 (2008)CrossRefGoogle Scholar
  18. 18.
    Chen, K., Huo, C., Zhou, Z., Lu, H., Cheng, J.: Semi-supervised change detection via Gaussian processes. In: Proc. IEEE IGARSS, pp. II-996–II-999 (2009)Google Scholar
  19. 19.
    Roy, M., Ghosh, S., Ghosh, A.: A novel approach for change detection of remotely sensed images using semi-supervised multiple classifier system. Inf. Sci. 269, 35–47 (2014)CrossRefGoogle Scholar
  20. 20.
    Ghosh, S., Roy, M., Ghosh, A.: Semi-supervised change detection using modified self-organizing feature map neural network. Appl. Soft Comput. 15, 1–20 (2014)CrossRefGoogle Scholar
  21. 21.
    Bovolo, F.: A multilevel parcel-based approach to change detection in very high resolution multitemporal images. IEEE Geosci. Remote Sens. Lett. 6(1), 33–37 (2009)CrossRefGoogle Scholar
  22. 22.
    Falco, N., Mura, M.D., Bovolo, F., Benediktsson, J.A., Bruzzone, L.: Change detection in VHR images based on morphological attribute profiles. IEEE Geosci. Remote Sens. Lett. 10(3), 636–640 (2013)CrossRefGoogle Scholar
  23. 23.
    Pacifici, F., Del Frate, F.: Automatic change detection in very high resolution images with pulse-coupled neural networks. IEEE Geosci. Remote Sens. Lett. 7(1), 58–62 (2010)Google Scholar
  24. 24.
    Liu, S., Bruzzone, L., Bovolo, F., Du, P.: Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Trans. Geosci. Remote Sens. 53(1), 244–260 (2015)CrossRefGoogle Scholar
  25. 25.
    Huang, X., Zhang, L.: A novel automatic change detection method for urban high-resolution remotely sensed imagery based on multiindex scene representation. IEEE Trans. Geosci. Remote Sens. 54(1), 609–625 (2016)CrossRefGoogle Scholar
  26. 26.
    Soille, P.: Morphological Image Analysis: Principle and Applications, 2nd edn. Springer, Berlin (2003)zbMATHGoogle Scholar
  27. 27. (accessed on February 2016)
  28. 28.
    Hay, G.J., Blaschke, T., Marceau, D.J., Bouchard, A.: A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS J. Photogramm. Remote Sens. 57(5), 327–345 (2003)CrossRefGoogle Scholar
  29. 29.
    Suresh, A., Shunmuganathan, K.L.: Image texture classification using gray level co-occurrence matrix based statistical features. Eur. J. Sci. Res. 75(4), 591–597 (2012)Google Scholar
  30. 30.
    Definiens Developer 7, Reference Book. Definiens AG, Munich, Germany (2007)Google Scholar
  31. 31.
    Pickett-Heaps, C.A., et al.: Evaluation of six satellite-derived fraction of absorbed photosynthetic active radiation (FAPAR) products across the Australian continent. Remote Sens. Environ. 140, 241–256 (2014)CrossRefGoogle Scholar
  32. 32.
    “Indian cities by investment climate”, Confederation of Indian Industry, Retrieved 30 August 2011Google Scholar
  33. 33.
    Huang, X., Zhang, L.: Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 5(1), 161–172 (2012)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)CrossRefGoogle Scholar
  35. 35.
    Brink, A.D., Pendock, N.E.: Minimum cross-entropy threshold selection. Pattern Recognit. 29(1), 179–188 (1996)CrossRefGoogle Scholar
  36. 36.
    Baraldi, A., Parmiggiani, F.: An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Trans. Geosci. Remote Sens. 33(2), 293–304 (1995)CrossRefGoogle Scholar
  37. 37.
    Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  38. 38.
    Ouma, Y.O., Tetuko, J., Tateishi, R.: Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery. Int. J. Remote Sens. 29(12), 3417–3456 (2008)CrossRefGoogle Scholar
  39. 39.
    Dalla Mura, M., Atli Benediktsson, J., Waske, B., Bruzzone, L.: Extended profiles with morphological attribute filters for the analysis of hyperspectral data. Int. J. Remote Sens. 31(22), 5975–5991 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringGovernment College of TechnologyCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringGovernment College of TechnologyCoimbatoreIndia

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