1. Benz, U. C; Hofmann, P; Willhauck, G; Lingenfelder, I; Heynen, M (2004). "Multiresolution, object-oriented fuzzy analysis of remote sensing data for GISready information". ISPRS Journal of Photogrammetry and Remote Sensing, 58: 239-258 [DOI:10.1016/j.isprsjprs.2003.10.002]

2. Blaschke, T. (2010). "Object based image analysis for remote sensing". ISPRS journal of photogrammetry and remote sensing, 65(1): 2-16. [DOI:10.1016/j.isprsjprs.2009.06.004]

3. Boudaghpour, S., Bagheri, M., & Bagheri, Z. (2015). Estimation of flood environmental effects using flood zone mapping techniques in Halilrood Kerman, Iran. Arabian Journal for Science and Engineering, 40(3), 659-675. [DOI:10.1007/s13369-014-1536-2]

4. Chen, M.; Su, W.; Li, L.; Zhang, C.; Yue, A.; Li, H (2009). "Comparison of pixel-based and object-oriented knowledge-based classification methods using SPOT5 imagery". WSEAS Transactions on Information Science and Applications, 6(3): 477-489.

5. Coginiton Developer 2012: Userguide, eCoginiton Developer Imaging

6. DeVries, B., Huang, C., Armston, J., Huang, W., Jones, J. W., & Lang, M. W. (2020). Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240, 111664. [DOI:10.1016/j.rse.2020.111664]

7. Drăguţ, L., & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3), 330-344. [DOI:10.1016/j.geomorph.2006.04.013]

8. Drăguţ, L., & Blaschke, T. (2006). Automated classification of landform elements using object-based image analysis. Geomorphology, 81(3), 330-344. [DOI:10.1016/j.geomorph.2006.04.013]

9. Drǎguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859-871. [DOI:10.1080/13658810903174803]

10. Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., & Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114. [DOI:10.1016/j.rse.2014.07.028]

11. Fassnacht, F. E., Hartig, F., Latifi, H., Berger, C., Hernández, J., Corvalán, P., & Koch, B. (2014). Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass. Remote Sensing of Environment, 154, 102-114. [DOI:10.1016/j.rse.2014.07.028]

12. GAO, Y., Mas, J. F., Kerle, N., & Navarrete Pacheco, J. A. (2011). Optimal region growing segmentation and its effect on classification accuracy. International journal of remote sensing, 32(13), 3747-3763. [DOI:10.1080/01431161003777189]

13. GAO, Y.; Mas.J.F. Navarrete.A (2009). "The improvement of an objectoriented classification using multi-temporal MODIS EVI satellite data", International Journal of Digital Earth, Volume 2, Issue 3 September 2009: 219 - 236. [DOI:10.1080/17538940902818311]

14. Haq, M., Akhtar, M., Muhammad, S., Paras, S., & Rahmatullah, J. (2012). Techniques of Remote Sensing and GIS for flood monitoring and damage assessment: A case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 15(2), 135-141. [DOI:10.1016/j.ejrs.2012.07.002]

15. Jensen, J.R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. 2d ed. Englewood Cliffs, New Jersey: Prentice-Hall.

16. Karimzadeh, S.; Matsuoka, M. A Weighted Overlay Method for Liquefaction-Related Urban Damage Detection: A Case Study of the 6 September 2018 Hokkaido Eastern Iburi Earthquake, Japan. Geosciences 2018, 8, 487. https://doi.org/10.3390/geosciences8120487 [DOI:10.3390/geosciences8120487.]

17. Karimzadeh, S.; Matsuoka, M. Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring. Sensors 2020, 20, 4751. https://doi.org/10.3390/s20174751 [DOI:10.3390/s20174751.] [PMID] [PMCID]

18. Lillesand, T.; Kiefer, R, W.; Chipman, J (2001). Remote sensing and image interpretation, 6th Edition, Wiley Publication, Washington.

19. Mohammadi, A., Kamran, K. V., Karimzadeh, S., Shahabi, H., & Al-Ansari, N. (2020). Flood detection and susceptibility mapping using sentinel-1 time series, alternating decision trees, and bag-adtree models. Complexity, 2020. [DOI:10.1155/2020/4271376]

20. Navulur, K. (2006). Multispectral image analysis using the object-oriented paradigm. CRC press. [DOI:10.1201/9781420043075]

21. Oruc M, Marangoz AM & Buyuksalih G 2004. Comparison of pixel-based and object-oriented classification approaches using Landsat-7 ETM spectral bands.

22. Pistrika, A. (2010). Flood damage estimation based on flood simulation scenarios and a GIS platform. European Water, 30, 3-11.‌

23. Rego LFG & Koch B 2003. Automatic classification of land cover with high resolution data of the Rio De Janeiro City Brazil. Comparison between pixel and object classification. [online] available at: http://www.definiens.com/documents/publications_earth2003.php Accessed 9 [DOI:10.1109/DFUA.2003.1219981]

24. Singha, M., Dong, J., Sarmah, S., You, N., Zhou, Y., Zhang, G., & Xiao, X. (2020). Identifying floods and flood-affected paddy rice fields in Bangladesh based on Sentinel-1 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 278-293. [DOI:10.1016/j.isprsjprs.2020.06.011]

25. Turan, B., & Wang, K. H. (2014). An object-oriented overland flow solver for watershed flood inundation predictions: case study of Ulus basin, Turkey. Journal of Hydrology and Hydromechanics, 62(3), 209.‌ [DOI:10.2478/johh-2014-0029]

26. Uddin, K., Matin, M. A., & Meyer, F. J. (2019). Operational flood mapping using multi-temporal sentinel-1 SAR images: a case study from Bangladesh. Remote Sensing, 11(13), 1581. [DOI:10.3390/rs11131581]

27. Noraisefat, I., Nazari, S., & Karimi, S. (2016). Change of the physical development of the city of Rasht and the surrounding land evaluation land use changes with satellite images. Journal of Geography and Environmental Studies, 5(17), 21-32.‌

28. Volschenk, T., Fey, M. V., & Zietsman, H. L. (2005). Situation Analysis of Problems for Water Quality Management in the Lower Orange River Region with Special Reference to the Contribution of the Foothills to Salinization. Pretoria: Water Research Commission.

29. Willhauck, G., Schneider, T., De Kok, R., & Ammer, U. (2000, July). Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. In Proceedings of XIX ISPRS congress (Vol. 33, pp. 35-42).

30. Yan, GAO. 2003, Pixel Based and Object Oriented Image for Coal Fire Research.