Skip to main content
SpringerLink
Log in

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

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Brook, A.: Three-dimensional wavelets-based denoising of hyperspectral imagery. J. Electron. Imaging 24(1), 013034 (2015)

    Article  Google Scholar 

  8. Walter, V.: Object-based classification of remote sensing data for change detection. ISPRS J. Photogramm. Remote Sens. 58(3/4), 225–238 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Celik, T.: Multi scale change detection in multitemporal satellite images. IEEE Geosci. Remote Sens. Lett. 6(4), 820–824 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  26. Soille, P.: Morphological Image Analysis: Principle and Applications, 2nd edn. Springer, Berlin (2003)

    MATH  Google Scholar 

  27. http://earthexplorer.usgs.gov/ (accessed on February 2016)

  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)

    Article  Google Scholar 

  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. Definiens Developer 7, Reference Book. Definiens AG, Munich, Germany (2007)

  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)

    Article  Google Scholar 

  32. “Indian cities by investment climate”, Confederation of Indian Industry, Retrieved 30 August 2011

  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)

    Article  MathSciNet  Google Scholar 

  34. Youden, W.J.: Index for rating diagnostic tests. Cancer 3(1), 32–35 (1950)

    Article  Google Scholar 

  35. Brink, A.D., Pendock, N.E.: Minimum cross-entropy threshold selection. Pattern Recognit. 29(1), 179–188 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  37. Haralick, R.M., Shanmugam, K., Dinstein, I.H.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamilnadu, 641 013, India

    R. Kishorekumar

  2. Department of Electronics and Communication Engineering, Government College of Technology, Coimbatore, Tamilnadu, 641 013, India

    P. Deepa

Authors
  1. R. Kishorekumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Deepa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Kishorekumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kishorekumar, R., Deepa, P. Automatic change detection using multiindex information map on high-resolution remote sensing images. Cluster Comput 21, 39–49 (2018). https://doi.org/10.1007/s10586-017-0917-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-0917-1

Keywords

Search

Navigation