Automatic Semantic Segmentation for Change Detection in Remote Sensing Images

  • Tejashree Kulkarni
  • N Venugopal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 705)


Change detection (CD) mainly focuses on the extraction of change information from multispectral remote sensing images of the same geographical location for environmental monitoring, natural disaster evaluation, urban studies, and deforestation monitoring. While capturing the Landsat imagery, there may occur data missing issues such as occlusion of cloud, camera sensor, and aperture artifacts. The existing machine learning approaches do not provide significant results. This paper proposes a DeepLab Dilated convolutional neural network (DL-DCNN) for semantic segmentation with the goal to occur the change map for earth observation applications. Experimental results reveal that the accuracy of the proposed change detection results provides improved results as compared with the existing algorithms and maps the semantic objects within the predefined class as change or no change.


Change detection Remote sensing Multispectral Deep learning 


  1. 1.
    El-Kawy, O.A., Rød, J.K., Ismail, H.A., Suliman, A.S.: Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data. Appl. Geogr. 31(2), 483–494 (2011)CrossRefGoogle Scholar
  2. 2.
    Fichera, C.R., Modica, G., Pollino, M.: Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. Eur. J. Remote Sens. 45(1), 1–18 (2012)CrossRefGoogle Scholar
  3. 3.
    Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., Xian, G.: A comprehensive change detection method for updating the national land cover database to circa 2011. Remote Sens. Environ. 132, 159–175 (2013)CrossRefGoogle Scholar
  4. 4.
    Zhu, Z., Woodcock, C.E.: Continuous change detection and classification of land cover using all available Landsat data. Remote Sens. Environ. 144, 152–171 (2014)CrossRefGoogle Scholar
  5. 5.
    Hao, M., Shi, W., Zhang, H., Li, C.: Unsupervised change detection with expectation-maximization-based level set. IEEE Geosci. Remote Sens. Lett. 11(1), 210–214 (2014)CrossRefGoogle Scholar
  6. 6.
    Wu, C., Zhang, L., Zhang, L.: A scene change detection framework for multi-temporal very high resolution remote sensing images. Sig. Process. 124, 184–197 (2016)CrossRefGoogle Scholar
  7. 7.
    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. Sel. Top. Appl. Earth Obs. Remote Sens. 7(8), 3525–3533 (2014)CrossRefGoogle Scholar
  8. 8.
    Liu, Z.G., Dezert, J., Mercier, G., Pan, Q.: Dynamic evidential reasoning for change detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 50(5), 1955–1967 (2012)CrossRefGoogle Scholar
  9. 9.
    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. Photogram. Remote Sens. 80, 91–106 (2013)CrossRefGoogle Scholar
  10. 10.
    Du, P., Liu, S., Xia, J., Zhao, Y.: Information fusion techniques for change detection from multi-temporal remote sensing images. Inf. Fusion 14(1), 19–27 (2013)CrossRefGoogle Scholar
  11. 11.
    Chen, G., Hay, G.J., Carvalho, L.M., Wulder, M.A.: Object-based change detection. Int. J. Remote Sens. 33(14), 4434–4457 (2012)CrossRefGoogle Scholar
  12. 12.
    Gong, M., Zhou, Z., Ma, J.: Change detection in synthetic aperture radar images based on image fusion and fuzzy clustering. IEEE Trans. Image Process. 21(4), 2141–2151 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Bovolo, F., Marchesi, S., Bruzzone, L.: A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Trans. Geosci. Remote Sens. 50(6), 2196–2212 (2012)CrossRefGoogle Scholar
  14. 14.
    Mishra, N.S., Ghosh, S., Ghosh, A.: Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Appl. Soft Comput. 12(8), 2683–2692 (2012)CrossRefGoogle Scholar
  15. 15.
    Gu, W., Lv, Z., Hao, M.: Change detection method for remote sensing images based on an improved Markov random field. Multimedia Tools Appl. 1–6 (2015)Google Scholar
  16. 16.
    Du, P., Liu, S., Gamba, P., Tan, K., Xia, J.: Fusion of difference images for change detection over urban areas. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 5(4), 1076–1086 (2012)CrossRefGoogle Scholar
  17. 17.
    Ghosh, A., Subudhi, B.N., Bruzzone, L.: Integration of Gibbs Markov random field and hopfield-type neural networks for unsupervised change detection in remotely sensed multitemporal images. IEEE Trans. Image Process. 22(8), 3087–3096 (2013)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Romero, A., Gatta, C., Camps-Valls, G.: Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans. Geosci. Remote Sens. 54(3), 1349–1362 (2016)CrossRefGoogle Scholar
  19. 19.
    Leichtle, T., Geiß, C., Wurm, M., Lakes, T., Taubenböck, H.: Unsupervised change detection in VHR remote sensing imagery–an object-based clustering approach in a dynamic urban environment. Int. J. Appl. Earth Obs. Geoinf. 54, 15–27 (2017)CrossRefGoogle Scholar
  20. 20.
    Su, L., Gong, M., Zhang, P., Zhang, M., Liu, J., Yang, H.: Deep learning and mapping based ternary change detection for information unbalanced images. Pattern Recogn. 66, 213–228 (2017)CrossRefGoogle Scholar
  21. 21.
    Zhang, P., Gong, M., Su, L., Liu, J., Li, Z.: Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images. ISPRS J. Photogram. Remote Sens. 116, 24–41 (2016)CrossRefGoogle Scholar
  22. 22.
    Lu, X., Yuan, Y., Zheng, X.: Joint dictionary learning for multispectral change detection. IEEE Trans. Cybern. 47(4), 884–897 (2017)CrossRefGoogle Scholar
  23. 23.
    De Morsier, F., Tuia, D., Borgeaud, M., Gass, V., Thiran, J.P.: Semi-supervised novelty detection using SVM entire solution path. IEEE Trans. Geosci. Remote Sens. 51(4), 1939–1950 (2013)CrossRefGoogle Scholar
  24. 24.
    Wu, C., Du, B., Zhang, L.: Slow feature analysis for change detection in multispectral imagery. IEEE Trans. Geosci. Remote Sens. 52(5), 2858–2874 (2014)CrossRefGoogle Scholar
  25. 25.
    Lu, X., Yuan, Y., Zheng, X.: Joint dictionary learning for multispectral change detection. IEEE Trans. Cybern. 47(4), 884–897 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Electrical and Electronics Engineering [Embedded System]PES UniversityBengaluruIndia
  2. 2.Department of EEEPES UniversityBengaluruIndia

Personalised recommendations