A Hybrid Algorithm for Urban LULC Change Detection for Building Smart-city by Using WorldView Images

REFERENCES

• R. V. Dey, Y. Zhang, and M. Zhong, “A review on image segmentation techniques with remote sensing perspective,” in Proceedings of the International Society for Photogrammetry and Remote Sensing Symposium (ISPR, S10), XXXVIII (Part 7A).
   Google Scholar
• D. Zhang, M. M. Islam, and G. Lu, “A review on automatic image annotation techniques,” Pattern Recognit., Vol. 45, no. 1, pp. 346–62, 2012. doi:10.1016/j.patcog.2011.05.013.
   Web of Science ®Google Scholar
• P. Mitra, B. Uma Shankar, and S. K. Pal, “Segmentation of multispectral remote sensing images using active support vector machines,” Pattern Recognit. Lett., Vol. 25, no. 9, pp. 1067–74, 2004. doi:10.1016/j.patrec.2004.03.004.
   Web of Science ®Google Scholar
• K. Radhika, and S. Varadarajan, “A neural network based classification of satellite images for change detection applications,” Cogent Eng., Vol. 5, no. 1, pp. 1484587, 2018. doi:10.1080/23311916.2018.1484587.
   Web of Science ®Google Scholar
• J. Yan, L. Wang, W. Song, Y. Chen, X. Chen, and Z. Deng, “A time-series classification approach based on change detection for rapid land cover mapping,” ISPRS. J. Photogramm. Remote. Sens., Vol. 158, pp. 249–62, 2019. 0924-2716. doi:10.1016/j.isprsjprs.2019.10.003.
   Web of Science ®Google Scholar
• T. H. G. Tran, C. Ressl, and N. Pfeifer, “Integrated change detection and classification in urban areas based on airborne laser scanning point clouds,” Sensors, Vol. 18, no. 2, p. 448, 2018. doi:10.3390/s18020448.
   PubMed Web of Science ®Google Scholar
• S. Suresh, and S. Lal, “An efficient cuckoo search algorithm based multilevel thresholding for segmentation of satellite images using different objective functions,” Expert. Syst. Appl., Vol. 58, pp. 184–209, 2016. doi:10.1016/j.eswa.2016.03.032.
   Web of Science ®Google Scholar
• Z. Xing, “An improved emperor penguin optimization based multilevel thresholding for color image segmentation,” Knowl. Based. Syst., Vol. 194, p. 105570, 2020. doi:10.1016/j.knosys.2020.105570. http://www.sciencedirect.com/science/article/pii/S0950705120300514.
   Web of Science ®Google Scholar
• Y. Li, S. Feng, X. Zhang, and L. Jiao, “SAR image segmentation based on quantum-inspired multi-objective evolutionary clustering algorithm,” Inf. Process. Lett., Vol. 114, no. 6, pp. 287–93, 2014. doi:10.1016/j.ipl.2013.12.010.
   Web of Science ®Google Scholar
• S. J. Mousavirad, and H. Ebrahimpour-Komleh, “Human mental search-based multilevel thresholding for image segmentation,” Appl. Soft. Comput., Vol. 97, pp. 105427, 2020. doi:10.1016/j.asoc.2019.04.002. http://www.sciencedirect.com/science/article/pii/S1568494619301838.
   Web of Science ®Google Scholar
• A. H. Chughtai, H. Abbasi, and I. R. Karas, “A review on change detection method and accuracy assessment for land use land cover,” Remote Sens. Appl. Soc. Environ., Vol. 22, p. 100482, 2021. ISSN 2352-9385. doi:10.1016/j.rsase.2021.100482.
   Google Scholar
• M. Weigand, J. Staab, M. Wurm, and H. Taubenböck, “Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution sentinel-2 data,” Int. J. Appl. Earth Obs. Geoinf., Vol. 88, p. 102065, 2020. ISSN 1569-8432. doi:10.1016/j.jag.2020.102065.
   Web of Science ®Google Scholar
• P. Saha, R. Mitra, K. Chakraborty, and M. Roy, “Application of multi layer perceptron neural network Markov chain model for LULC change detection in the Sub-Himalayan North Bengal,” Remote Sens. Appl. Soc. Environ., Vol. 26, p. 100730, 2022. ISSN 2352-9385. doi:10.1016/j.rsase.2022.100730.
   Google Scholar
• K. T. Singh, N. M. Singh, and T. T. Devi, “A remote sensing, GIS based study on LULC change detection by different methods of classifiers on landsat data,” in Innovative Trends in Hydrological and Environmental Systems. Lecture Notes in Civil Engineering, Vol 234, A.K. Dikshit, B. Narasimhan, B. Kumar, and A.K. Patel, Eds. Singapore: Springer, 2022, pp. 107–17. doi:10.1007/978-981-19-0304-5_9
   Google Scholar
• R. Pal, S. Mukhopadhyay, D. Chakraborty, and P. N. Suganthan, “Very high-resolution satellite image segmentation using variable-length multi-objective genetic clustering for multi-class change detection,” J. King Saud Univ. – Comput. Inform. Sci., Vol. 34, pp. 9964–76, 2022. ISSN 1319-1578. doi:10.1016/j.jksuci.2021.12.023.
   Web of Science ®Google Scholar
• S. M. Mechkouri, S. El Joumani, R. Zennouhi, and L. Masmoudi, “Multi-objective optimization for worldview image segmentation funded on the entropies of tsallis and rényi,” Multimed. Tools. Appl., Vol. 79, pp. 30637–52, 2020. doi:10.1007/s11042-020-09572-4.
   Web of Science ®Google Scholar
• A. Alam, M. S. Bhat, and M. Maheen, “Using landsat satellite data for assessing the land use and land cover change in Kashmir valley,” GeoJournal, Vol. 85, pp. 1529–43, 2020. doi:10.1007/s10708-019-10037-x.
   Web of Science ®Google Scholar
• P. K. Mishra, A. Rai, and S. C. Rai, “Land use and land cover change detection using geospatial techniques in the Sikkim Himalaya, India,” Egyptian J. Remote Sens. Space Sci., Vol. 23, no. 2, pp. 133–43, 2020. ISSN 1110-9823. doi:10.1016/j.ejrs.2019.02.001.
   Web of Science ®Google Scholar
• L. Xu, W. Jing, H. Song, and G. Chen, “High-resolution remote sensing image change detection combined with pixel-level and object-level,” IEEE. Access., Vol. 7, pp. 78909–18, 2019. doi:10.1109/ACCESS.2019.2922839.
   Web of Science ®Google Scholar