1. [1] Sadeghi, Vahid. "Development of a fuzzy thresholding technique for automatic change detection using satellite images." PhD Thesis in Geomatics Engineering, K.N. Toosi University of Technology Faculty of Geodesy and Geomatics, 2016.

2. [2] Singh, A., Review article digital change detection techniques using remotely-sensed data. International journal of remote sensing, 10(6), pp.989-1003, 1989. [DOI:10.1080/01431168908903939]

3. [3] Hussain, M., Chen, D., Cheng, A., Wei, H. and Stanley, D., Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, pp.91-106, 2016. [DOI:10.1016/j.isprsjprs.2013.03.006]

4. [4] Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J. and Xian, G., A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132, pp.159-175, 2013. [DOI:10.1016/j.rse.2013.01.012]

5. [5] Tan, B., Masek, J.G., Wolfe, R., Gao, F., Huang, C., Vermote, E.F., Sexton, J.O. and Ederer, G., Improved forest change detection with terrain illumination corrected Landsat images. Remote Sensing of Environment, 136, pp.469-483, 2013. [DOI:10.1016/j.rse.2013.05.013]

6. [6] Dong, L. and Shan, J., A comprehensive review of earthquake-induced building damage detection with remote sensing techniques. ISPRS Journal of Photogrammetry and Remote Sensing, 84, pp.85-99, 2013. [DOI:10.1016/j.isprsjprs.2013.06.011]

7. [7] Dube, T., Gumindoga, W. and Chawira, M., Detection of land cover changes around Lake Mutirikwi, Zimbabwe, based on traditional remote sensing image classification techniques. African Journal of Aquatic Science, 39(1), pp.89-95, 2014. [DOI:10.2989/16085914.2013.870068]

8. [8] Arnett, J.T., Coops, N.C., Daniels, L.D. and Falls, R.W., Detecting forest damage after a low-severity fire using remote sensing at multiple scales. International Journal of Applied Earth Observation and Geoinformation, 35, pp.239-246, 2015. [DOI:10.1016/j.jag.2014.09.013]

9. [9] Sun, C., Wu, Z.F., Lv, Z.Q., Yao, N. and Wei, J.B., Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 21, pp.409-417, 2013. [DOI:10.1016/j.jag.2011.12.012]

10. [10] Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B. and Lambin, E., Review ArticleDigital change detection methods in ecosystem monitoring: a review. International journal of remote sensing, 25(9), pp.1565-1596, 2004. [DOI:10.1080/0143116031000101675]

11. [11] Deer, P., Digital change detection techniques in remote sensing, 1995.

12. [12] Lu, D., Mausel, P., Brondizio, E. and Moran, E., Change detection techniques. International journal of remote sensing, 25(12), pp.2365-2401, 2004. [DOI:10.1080/0143116031000139863]

13. [13] Celik, T. and Yetgin, Z., Change detection without difference image computation based on multiobjective cost function optimization. Turkish Journal of Electrical Engineering & Computer Sciences, 19(6), pp.941-956, 2011.

14. [14] Stathaki, T., Image fusion: algorithms and applications. Academic Press, 2011.

15. [15] Wang, Z., Alan. C. Bovik,"A Universal Quality Index". IEEE Signal Processing Letters, 20, pp.1-4, 2002.

16. [16] [16] Rignot, E.J. and Van Zyl, J.J., Change detection techniques for ERS-1 SAR data. IEEE Transactions on Geoscience and Remote sensing, 31(4), pp.896-906, 1993. [DOI:10.1109/36.239913]

17. [17] Otsu, N., A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1), pp.62-66, 1979. [DOI:10.1109/TSMC.1979.4310076]

18. [18] Ye, Z., Hu, Z., Lai, X. and Chen, H., Image segmentation using thresholding and swarm intelligence. Journal of Software, 7(5), pp.1074-1082, 2012. [DOI:10.4304/jsw.7.5.1074-1082]

19. [19] Li, L., Gong, R. and Chen, W., Gray level image thresholding based on fisher linear projection of two-dimensional histogram. Pattern Recognition, 30(5), pp.743-749, 1997. [DOI:10.1016/S0031-3203(96)00100-8]

20. [20] Liu, G., Delon, J., Gousseau, Y. and Tupin, F., August. Unsupervised change detection between multi-sensor high resolution satellite images. In Signal Processing Conference (EUSIPCO), 2016 24th European (pp. 2435-2439). IEEE, 2016. [DOI:10.1109/EUSIPCO.2016.7760686]

21. [21] Khan, A., Thakre, P., Pathan, S. and Principal, H.O.D., Unsupervised Change Detection Algorithm from VHR Satelite Images using Soft Computing Technique. International Journal of Engineering Science, 12355, 2017.

22. [22] Leichtle, T., Geiß, C., Wurm, M., Lakes, T. and Taubenböck, H., Unsupervised change detection in VHR remote sensing imagery-an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, pp.15-27, 2017. [DOI:10.1016/j.jag.2016.08.010]

23. [23] Leite, L.R., Carvalho, L.M.T.D. and Silva, F.M.D., Change detection in forest and savannas using statistical analysis based on geographical objects. Boletim de Ciências Geodésicas, 23(2), pp.284-295, 2017. [DOI:10.1590/s1982-21702017000200018]

24. [24] Ma, L., Li, M., Blaschke, T., Ma, X., Tiede, D., Cheng, L., Chen, Z. and Chen, D., Object-based change detection in urban areas: the effects of segmentation strategy, scale, and feature space on unsupervised methods. Remote Sensing, 8(9), p.761, 2016. [DOI:10.3390/rs8090761]