1. [1] M. S. Moussavi et al., "Derivation and validation of supraglacial lake volumes on the Greenland Ice Sheet from high-resolution satellite imagery," Remote sensing of environment, vol. 183, pp. 294-303, 2016. [ DOI:10.1016/j.rse.2016.05.024] 2. [2] X.-P. Song et al., "National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey," Remote sensing of environment, vol. 190, pp. 383-395, 2017. [ DOI:10.1016/j.rse.2017.01.008] 3. [3] Y.-S. Hsiao et al., "High-resolution depth and coastline over major atolls of South China Sea from satellite altimetry and imagery," Remote Sensing of Environment, vol. 176, pp. 69-83, 2016. [ DOI:10.1016/j.rse.2016.01.016] 4. [4] R. Colombo, D. Bellingeri, D. Fasolini, and C. M. Marino, "Retrieval of leaf area index in different vegetation types using high resolution satellite data," Remote Sensing of Environment, vol. 86, no. 1, pp. 120-131, 2003. [ DOI:10.1016/S0034-4257(03)00094-4] 5. [5] P. M. Dare, "Shadow analysis in high-resolution satellite imagery of urban areas," Photogrammetric Engineering & Remote Sensing, vol. 71, no. 2, pp. 169-177, 2005. [ DOI:10.14358/PERS.71.2.169] 6. [6] Y. Qian, W. Zhou, W. Yu, and S. T. Pickett, "Quantifying spatiotemporal pattern of urban greenspace: new insights from high resolution data," Landscape ecology, vol. 30, no. 7, pp. 1165-1173, 2015. [ DOI:10.1007/s10980-015-0195-3] 7. [7] M. Vakalopoulou, K. Karantzalos, N. Komodakis, and N. Paragios, "Building detection in very high resolution multispectral data with deep learning features," in 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2015: IEEE, pp. 1873-1876. [ DOI:10.1109/IGARSS.2015.7326158] 8. [8] A. Albert, J. Kaur, and M. C. Gonzalez, "Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale," in Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 2017: ACM, pp. 1357-1366. [ DOI:10.1145/3097983.3098070] 9. [9] Y. Qian, W. Zhou, W. Li, and L. Han, "Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images," Urban Forestry & Urban Greening, vol. 14, no. 1, pp. 39-47, 2015. [ DOI:10.1016/j.ufug.2014.11.006] 10. [10] X. Tong et al., "Building-damage detection using pre-and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 68, pp. 13-27, 2012. [ DOI:10.1016/j.isprsjprs.2011.12.004] 11. [11] D. Brunner, G. Lemoine, and L. Bruzzone, "Earthquake damage assessment of buildings using VHR optical and SAR imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 5, pp. 2403-2420, 2010. [ DOI:10.1109/TGRS.2009.2038274] 12. [12] P. Li, H. Xu, and J. Guo, "Urban building damage detection from very high resolution imagery using OCSVM and spatial features," International Journal of Remote Sensing, vol. 31, no. 13, pp. 3393-3409, 2010. [ DOI:10.1080/01431161003727705] 13. [13] P. Li, H. Xu, and B. Song, "A novel method for urban road damage detection using very high resolution satellite imagery and road map," Photogrammetric Engineering & Remote Sensing, vol. 77, no. 10, pp. 1057-1066, 2011. [ DOI:10.14358/PERS.77.10.1057] 14. [14] T. Toutin, "Geometric processing of remote sensing images: models, algorithms and methods," International journal of remote sensing, vol. 25, no. 10, pp. 1893-1924, 2004. [ DOI:10.1080/0143116031000101611] 15. [15] C. V. Tao and Y. Hu, "A comprehensive study of the rational function model for photogrammetric processing," Photogrammetric engineering and remote sensing, vol. 67, no. 12, pp. 1347-1358, 2001. 16. [16] M. J. Valadan Zoej, M. Mokhtarzade, A. Mansourian, H. Ebadi, and S. Sadeghian, "Rational function optimization using genetic algorithms," International journal of applied earth observation and geoinformation, vol. 9, no. 4, pp. 403-413, 2007. [ DOI:10.1016/j.jag.2007.02.002] 17. [17] S. H. Alizadeh Moghaddam, M. Mokhtarzade, and S. A. Alizadeh Moghaddam, "Optimization of RFM's Structure Based on PSO Algorithm and Figure Condition Analysis," IEEE Geoscience and Remote Sensing Letters, vol. 15, no. 8, pp. 1179-1183, 2018. [ DOI:10.1109/LGRS.2018.2829598] 18. [18] Y. Xiuxiao and L. Xianyong, "A method for solving rational polynomial coefficients based on ridge estimation," Geomatics and Information Science of Wuhan University, vol. 33, no. 11, pp. 1130-1133, 2008. 19. [19] Q. Zhou, W. Jiao, and T. Long, "Solution to the rational function model based on the Levenberg-Marquardt algorithm," in 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, 2012: IEEE, pp. 2795-2799. [ DOI:10.1109/FSKD.2012.6234281] 20. [20] X. Wang, D. Liu, Q. Zhang, and H. Huang, "The iteration by correcting characteristic value and its application in surveying data processing," J. Heilongjiang Inst. Technol, vol. 15, no. 2, pp. 3-6, 2001. 21. [21] Z. Xiong and Y. Zhang, "Bundle adjustment with rational polynomial camera models based on generic method," IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 1, pp. 190-202, 2010. [ DOI:10.1109/TGRS.2010.2054833] 22. [22] Y. Wu and Y. Ming, "A fast and robust method of calculating RFM parameters for satellite imagery," Remote sensing letters, vol. 7, no. 12, pp. 1112-1120, 2016. [ DOI:10.1080/2150704X.2016.1219459] 23. [23] Z. Li-ping, L. Feng-de, L. Jian, and W. Wei, "Research on Reducing Term of Higher Order in RFM Model," Science of Surveying and Mapping, vol. 32, no. 4, p. 14, 2007. 24. [24] Y. Zhang, Y. Lu, L. Wang, and X. Huang, "A new approach on optimization of the rational function model of high-resolution satellite imagery," IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 7, pp. 2758-2764, 2011. [ DOI:10.1109/TGRS.2011.2174797] 25. [25] L. Tengfei, J. Weili, and H. Guojin, "Nested regression based optimal selection (NRBOS) of rational polynomial coefficients," Photogrammetric Engineering & Remote Sensing, vol. 80, no. 3, pp. 261-269, 2014. [ DOI:10.14358/PERS.80.3.261] 26. [26] K. Sastry, D. Goldberg, and G. Kendall, "Genetic Algorithms," in Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques: Springer, 2014, pp. 93-117. [ DOI:10.1007/978-1-4614-6940-7_4] 27. [27] J. Kennedy, "Particle swarm optimization," in Encyclopedia of machine learning, vol. 10: Springer, 2010, pp. 760-766. 28. [28] M. Jannati and M. J. Valadan Zoej, "Introducing genetic modification concept to optimize rational function models (RFMs) for georeferencing of satellite imagery," GIScience & Remote Sensing, vol. 52, no. 4, pp. 510-525, 2015. [ DOI:10.1080/15481603.2015.1052634] 29. [29] M. Jannati, M. Valadan Zoej, and M. Mokhtarzade, "A Knowledge-Based Search Strategy for Optimally Structuring the Terrain Dependent Rational Function Models," Remote Sensing, vol. 9, no. 4, p. 345, 2017. [ DOI:10.3390/rs9040345] 30. [30] S. Yavari, M. J. V. Zoej, A. Mohammadzadeh, and M. Mokhtarzade, "Particle swarm optimization of RFM for georeferencing of satellite images," IEEE Geoscience and Remote Sensing Letters, vol. 10, no. 1, pp. 135-139, 2013. [ DOI:10.1109/LGRS.2012.2195153] 31. [31] S. Yavari, M. J. Valadan Zoej, M. R. Sahebi, and M. Mokhtarzade, "Accuracy improvement of high resolution satellite image georeferencing using an optimized line-based rational function model," International journal of remote sensing, vol. 39, no. 6, pp. 1655-1670, 2018. [ DOI:10.1080/01431161.2017.1410294] 32. [32] S. Gholinejad, A. Alizadeh Naeini, and A. Amiri-Simkooei, "Robust Particle Swarm Optimization of RFMs for High-Resolution Satellite Images Based on K-Fold Cross-Validation," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 8, pp. 2594 - 2599, 2019. [ DOI:10.1109/JSTARS.2018.2881382] 33. [33] J. Kennedy and R. C. Eberhart, "A discrete binary version of the particle swarm algorithm," in 1997 IEEE International conference on systems, man, and cybernetics. Computational cybernetics and simulation, 1997, vol. 5: IEEE, pp. 4104-4108.
|