Gamba, P. and Casciati, F. (1998) GIS and image understanding for near-real-time earthquake damage assessment. Photogrammetric Engineering and Remote Sensing, 64, 987-994.
Saito, K., Spence, R.J., Going, C., and Markus, M. (2004) Using high-resolution satellite images for post-earthquake building damage assessment: a study following the 26 January 2001 Gujarat earthquake. Earthquake Spectra, 20(1), 145-169.
Yamazaki, F., Yano, Y., andMatsuoka,M. (2005) Visual damage interpretation of buildings in bam city using quickbird images following the 2003 bam, Iran, earthquake. Earthquake Spectra, 21(S1), 329-336.
Hisada, Y., Shibayama,A., and Ghayamghamian, M.R. (2005) Building damage and seismic intensity in Bam City from the 2003 Iran, Bam Earthquake. Bulletin of Earthquake Research Institute, University of Tokyo, 79(3 & 4), 81-94.
Gusella, L.,Adams, B.J., Bitelli, G., Huyck, C.K., and Mognol, A. (2005) Object-oriented image understanding and post-earthquake damage assessment for the 2003 Bam, Iran, earthquake. Earthquake Spectra, 21(S1), 225-238.
www.ecognition.com.
Mansouri, B., Shinozuka, M., Huyck, C., and Houshmand, B. (2005) Earthquake-induced change detection in the 2003 Bam, Iran, earthquake by complex analysis using Envisat ASAR data. Ear thquake Spectra , 21(S1), 275-284.
Mansouri, B. and Hamednia, Y. (2015) A soft computing method for damage mapping using VHR optical satellite imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4935-4941.
Mostafazadeh, M. and Mansouri, B. (2015) Object-oriented building extraction from VHR satellite data and earthquake damage detection based on textural analysis using artificial neural network. Bulletin of Earthquake Science and Engineering, 2(1), 55-65.
http://www.gosur.com/google-earth.
Haralick, R.M., Shanmugam, K., and Dinstein, I.H. (1973) Textural features for image classification. IEEE Transactions on Systems, Man
and Cybernetics, SMC3(6), 610-621.