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Unsupervised Change Detection in Remote Sensing Images Using CDI and GIFP-FCM Clustering

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Proceedings of Fifth International Conference on Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 437))

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Abstract

In this paper, an unsupervised change detection method for remote sensing image is proposed. The method takes as input two bi temporal images and outputs a change detection map highlighting changed and unchanged areas. Initially, the method creates a feature vector space by performing the principal component analysis (PCA) of both the images. The first component of PCA of both the images is used to compute the combined difference image. A change map is then created from combined difference image by clustering it into two clusters changed and unchanged using GIFP-FCM clustering technique. The method is applied on Landsat 5 TM images and the results obtained are compared with other existing state of the arts method.

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References

  1. Khandelwal, P., Singh, K.K., Singh, B.K., Mehrotra, A.: Unsupervised change detection of multispectral images using Wavelet Fusion and Kohonen clustering network. Int. J. Eng. Technol. (IJET) 5(2), 1401–1406 (2013)

    Google Scholar 

  2. Singh, K.K., Mehrotra, A., Nigam, M.J., Pal, K.: Unsupervised change detection from remote sensing images using hybrid genetic FCM. In: IEEE Conference on engineering and systems, pp. 1–5, April 2013

    Google Scholar 

  3. Li, Y., Gong, M., Jiao, L., Li, L., Stolkin, R.: Change-detection map learning using matching pursuit. IEEE Trans. Geosci. Remote Sens. 1–12 (2015)

    Google Scholar 

  4. Neagoe, V.E., Stoica, R.M., Ciurea, A.I., Bruzzone, L., Bovolo, F.: Concurrent self-organizing maps for supervised/unsupervised change detection in remote sensing images. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 7(8), 1–12 (2014)

    Google Scholar 

  5. Bazi, Y., Bruzzone, L., Melgani, F.: An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images. IEEE Trans. Geosci. Remote Sens. 43(4), 874–887 (2005)

    Google Scholar 

  6. Ronggui, H.M.L.M.W.: Application of an improved Otsu algorithm in image segmentation. J. Electron. Meas. Instrum. 5, 011 (2010)

    Google Scholar 

  7. Coleman, G.B., Andrews, H.C.: Image segmentation by clustering. Proc. IEEE 67(5), 773–785 (1979)

    Google Scholar 

  8. Bruzzone, L., Prieto, D.: Automatic analysis of the difference image for unsupervised change detection. IEEE Trans. Geosci. Remote Sens. 38(3), 1171–1182 (2000)

    Google Scholar 

  9. Taneja, A., Ballan, L., Polleyfeys, M.: Geometric change detection in urban environments using images. J. Latex Class Files 6(1), 1–14 (2007)

    Google Scholar 

  10. Yetgin, Z.: Unsupervised change detection of satellite images using local gradual descent. IEEE Trans. Geosci. Remote Sens. 50(5), 1919–1929 (2012)

    Google Scholar 

  11. Klaric, M.N., Claywell, B.C., Scott, G.J., Hudson, N.J., Li, Y., Barratt, S.T., Keller, J.M., Davis, C.H.: GeoCDX: an automated change detection and exploitation system for high resolution satellite imagery. IEEE Trans. Geosci. Remote Sens. 51(4), 2067–2086 (2013)

    Google Scholar 

  12. Liu, Z., Mercier, G., Dezert, J., Pan, Q.: Change detection in heterogeneous remote sensing images based on multidimensional evidential reasoning. IEEE Geosci. Remote Sens. Lett. 11(1), 168–172 (2014)

    Google Scholar 

  13. Lu, J., Li, J., Chen, G., Xiong, B., Kuang, G.: Improving pixel-based change detection accuracy using an object-based approach in multitemporal SAR flood images. IEEE J. Selected Topics Appl. Earth Observ. Remote Sens. 1–11 (2015)

    Google Scholar 

  14. Celik, T.: Unsupervised change detection in satellite images using principal component analysis and k-means clustering. IEEE Geosci. Remote Sens. Lett. 6(4), 772–776 (2009)

    Google Scholar 

  15. Zheng, Y., Zhang, X., Hou, B., Liu, G.: Using combined difference image and k means clustering for SAR image change detection. IEEE Geosci. Remote Sens. Lett. 11(3), 691–695 (2014)

    Article  Google Scholar 

  16. Zhu, L., Chung, F.L., Wang, S.: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 39(3), 578–591 (2009)

    Article  Google Scholar 

  17. NASA GSFC, Alaska Circa 2005: Landsat TM Data, Feb. 2011. http://change.gsfc.nasa.gov/alaska.html

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Author information

Authors and Affiliations

  1. EEE Department, Dronacharya College of Engineering, Gurgaon, India

    Krishna Kant Singh & Monika Phulia

  2. The Northcap University, Gurgaon, India

    Akansha Singh

Authors
  1. Krishna Kant Singh
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  2. Akansha Singh
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  3. Monika Phulia
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Corresponding author

Correspondence to Krishna Kant Singh .

Editor information

Editors and Affiliations

  1. Dept of Applied Sci & Eng, Indian Instit of Tech Roorkee, Roorkee, India

    Millie Pant

  2. Department of Mathematics, Indian Inst of Tech Roorkee, Roorkee, India

    Kusum Deep

  3. Dept of Mathematics, South Asian University New Delhi, New Delhi, India

    Jagdish Chand Bansal

  4. Department of Mathematics and Comp Sci, Liverpool Hope University, LIVERPOOL, United Kingdom

    Atulya Nagar

  5. Department of Mathematics, National Inst of Tech Silchar, Silchar, Assam, India

    Kedar Nath Das

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© 2016 Springer Science+Business Media Singapore

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Singh, K.K., Singh, A., Phulia, M. (2016). Unsupervised Change Detection in Remote Sensing Images Using CDI and GIFP-FCM Clustering. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-10-0451-3_29

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  • DOI: https://doi.org/10.1007/978-981-10-0451-3_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-0450-6

  • Online ISBN: 978-981-10-0451-3

  • eBook Packages: EngineeringEngineering (R0)

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