Extracting Urban Built-up Areas from Optical and Radar Data Fusion using Machine Learning Algorithms

References

• Abida, K., et al., 2022. Sentinel-2 data for land use mapping: comparing different supervised classifications in semi-arid areas. Agriculture, 12 (9), 1429. doi:10.3390/agriculture12091429.
   Google Scholar
• Aduku, E., Eboh, I., and Egbuchulam, C. 2021. Urbanization and sustainable cities in nigeria. International Journal of Economics Development Research (IJEDR), 2 (1), 16–31. doi:10.37385/ijedr.v2i1.222.
   Google Scholar
• Akabane, G.K., et al., 2023. Reflections on disordered growth on urban mobility. Seven Editora. Available from: http://sevenpublicacoes.com.br/index.php/editora/article/view/2080.
   Google Scholar
• Anderson, K., et al. 2017. Earth observation in service of the 2030 agenda for sustainable development. Geo-Spatial Information Science, 20 (2), 77–96. doi:10.1080/10095020.2017.1333230.
   Web of Science ®Google Scholar
• Azmi, R., et al., 2023. A hybrid approach for extracting large-scale and accurate built-up areas using sar and multispectral data. Atmosphere, 14 (2), 240. doi:10.3390/atmos14020240.
   Web of Science ®Google Scholar
• Basheer, S., et al., 2022. Comparison of land use land cover classifiers using different satellite imagery and machine learning techniques. Remote Sensing, 14 (19), 4978. doi:10.3390/rs14194978.
   Web of Science ®Google Scholar
• Belgiu, M. and Drăguţ, L., 2016. Random forest in remote sensing: a review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31. doi:10.1016/j.isprsjprs.2016.01.011
   Web of Science ®Google Scholar
• Biswas, R.K., Kabir, E., and Khan, H.T.A. 2019. Causes of urban migration in Bangladesh: evidence from the urban health survey. Population Research and Policy Review, 38 (4), 593–614. doi:10.1007/s11113-019-09532-3.
   Web of Science ®Google Scholar
• Bramhe, V.S., Ghosh, S.K., and Garg, P.K., 2018. Extraction of built-up areas using convolutional neural networks and transfer learning from sentinel-2 satellite images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 79–85. doi:10.5194/isprs-archives-XLII-3-79-2018
   Google Scholar
• Bui, Q.-T., et al., 2022. Evaluation of sentinel-2/MSI atmospheric correction algorithms over two contrasted French coastal waters. Remote Sensing, 14 (5), 1099. doi:10.3390/rs14051099.
   Web of Science ®Google Scholar
• Cao, H., et al., 2018. Operational built-up areas extraction for cities in China using Sentinel-1 SAR data. Remote Sensing, 10 (6), 874. doi:10.3390/rs10060874.
   Web of Science ®Google Scholar
• Castro Gomez, M.G. 2017. Joint use of Sentinel-1 and Sentinel-2 for land cover classification: a machine learning approach. Lund University GEM Thesis Series.
   Google Scholar
• Chen, Y., et al., 2019. Fast neighbor search by using revised kd tree. Information Sciences, 472, 145–162. doi:10.1016/j.ins.2018.09.012
   Web of Science ®Google Scholar
• Congalton, R.G. and Green, K., 2019. Assessing the accuracy of remotely sensed data: principles and practices. 3rd ed. CRC press. doi:10.1201/9780429052729.
   Google Scholar
• Corbane, C., et al., 2021. Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery. Neural Computing and Applications, 33 (12), 6697–6720. doi:10.1007/s00521-020-05449-7.
   Web of Science ®Google Scholar
• Deliry, S.I., Avdan, Z.Y., and Avdan, U. 2021. Extracting urban impervious surfaces from Sentinel-2 and Landsat-8 satellite data for urban planning and environmental management. Environmental Science and Pollution Research, 28 (6), 6572–6586. doi:10.1007/s11356-020-11007-4.
   PubMed Web of Science ®Google Scholar
• Dervilis, N., et al., 2019. Nonlinear modal analysis via non‐parametric machine learning tools. Strain, 55 (1), e12297. doi:10.1111/str.12297.
   Web of Science ®Google Scholar
• Do Valle Junior, R.F., et al., 2023. The accuracy of land use and cover mapping across time in environmental disaster zones: the case of the b1 tailings dam rupture in Brumadinho, Brazil. Sustainability, 15(8), 6949.
   Web of Science ®Google Scholar
• Ettehadi Osgouei, P., et al., 2019. Separating built-up areas from bare land in mediterranean cities using Sentinel-2A imagery. Remote Sensing, 11 (3), 345. doi:10.3390/rs11030345.
   Web of Science ®Google Scholar
• Fakhri, F. and Gkanatsios, I., 2021. Integration of Sentinel-1 and Sentinel-2 data for change detection: A case study in a war conflict area of Mosul city. Remote Sensing Applications: Society & Environment, 22, 100505. doi:10.1016/j.rsase.2021.100505
   Google Scholar
• Foody, G.M. 2008. Harshness in image classification accuracy assessment. International Journal of Remote Sensing, 29 (11), 3137–3158. doi:10.1080/01431160701442120.
   Web of Science ®Google Scholar
• Forget, Y., et al., 2018. Complementarity between Sentinel-1 and Landsat 8 imagery for built-up mapping in Sub-Saharan Africa. Polymer Preprints, 2018100695.
   Google Scholar
• Ge, W., et al., 2018. Ghost city extraction and rate estimation in China based on NPP-VIIRS night-time light data. ISPRS International Journal of Geo-Information, 7 (6), 219. doi:10.3390/ijgi7060219.
   Web of Science ®Google Scholar
• Ghasemi, M., Karimzadeh, S., and Feizizadeh, B. 2021. Urban classification using preserved information of high dimensional textural features of Sentinel-1 images in Tabriz, Iran. Earth Science Informatics, 14 (4), 1745–1762. doi:10.1007/s12145-021-00617-2.
   Web of Science ®Google Scholar
• Haas, J. and Ban, Y., 2017. Sentinel-1A SAR and sentinel-2A MSI data fusion for urban ecosystem service mapping. Remote Sensing Applications: Society & Environment, 8, 41–53. doi:10.1016/j.rsase.2017.07.006
   Google Scholar
• Hafner, S., Ban, Y., and Nascetti, A., 2022. Unsupervised domain adaptation for global urban extraction using sentinel-1 SAR and sentinel-2 MSI data. Remote Sensing of Environment, 280, 113192. doi:10.1016/j.rse.2022.113192
   Web of Science ®Google Scholar
• Holobâcă, I.-H., Ivan, K., and Alexe, M. 2019. Extracting built-up areas from Sentinel-1 imagery using land-cover classification and texture analysis. International Journal of Remote Sensing, 40 (20), 8054–8069. doi:10.1080/01431161.2019.1608391.
   Web of Science ®Google Scholar
• Huang, M. and Jin, S. 2020. Rapid flood mapping and evaluation with a supervised classifier and change detection in Shouguang using Sentinel-1 SAR and Sentinel-2 optical data. Remote Sensing, 12 (13), 2073. doi:10.3390/rs12132073.
   Web of Science ®Google Scholar
• Iannelli, G.C. and Gamba, P., 2018. Jointly exploiting Sentinel-1 and Sentinel-2 for urban mapping. IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. 8209–8212. doi:10.1109/IGARSS.2018.8518172.
   Google Scholar
• Iannelli, G.C. and Gamba, P. 2019. Urban extent extraction combining sentinel data in the optical and microwave range. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12 (7), 2209–2216. doi:10.1109/JSTARS.2019.2920678.
   Web of Science ®Google Scholar
• Jensen, J.R., 1996. Thematic information extraction: Image classification. Introductory Digital Image Processing: A Remote Sensing Perspective. 2nd ed. Upper Saddle River, NJ: Prentice Hall, Inc., 197–256.
   Google Scholar
• Jensen, J.R., 2009. Remote sensing of the environment: an earth resource perspective 2/e. 2nd ed. India, New Delhi: Pearson Education India.
   Google Scholar
• Jo, J., Seo, J., and Fekete, J.-D., 2017. A progressive kd tree for approximate k-nearest neighbors. 2017 IEEE Workshop on Data Systems for Interactive Analysis (DSIA), Phoenix, United States. 1–5.
   Google Scholar
• Kai, P.M., et al., 2021. Effects of resampling image methods in sugarcane classification and the potential use of vegetation indices related to chlorophyll. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain, 1526–1531. doi:10.1109/COMPSAC51774.2021.00227.
   Google Scholar
• Kavvada, A., et al., 2020. Towards delivering on the sustainable development goals using earth observations. In: Remote Sensing of Environment. Vol. 247, Elsevier, 111930.
   Google Scholar
• Kebede, T.A., Hailu, B.T., and Suryabhagavan, K.V., 2022. Evaluation of spectral built-up indices for impervious surface extraction using sentinel-2A MSI imageries: a case of Addis Ababa city, Ethiopia. Environmental Challenges, 8, 100568. doi:10.1016/j.envc.2022.100568
   Google Scholar
• Khekare, G., et al., 2022. Testing and analysis of predictive capabilities of machine learning algorithms. In: Integrating meta-heuristics and machine learning for real-world optimization problems. Springer, 419–442.
   Google Scholar
• Kobayashi, S. and Ide, H. 2022. Rice crop monitoring using Sentinel-1 SAR data: A case study in Saku, Japan. Remote Sensing, 14 (14), 3254. doi:10.3390/rs14143254.
   Web of Science ®Google Scholar
• Li, T., et al., 2022. Built-Up area extraction from GF-3 SAR data based on a dual-attention transformer model. Remote Sensing, 14 (17), 4182. doi:10.3390/rs14174182.
   Web of Science ®Google Scholar
• Li, Y., et al., 2018. Evaluation of Sentinel-2A surface reflectance derived using Sen2Cor in North America. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11 (6), 1997–2021. doi:10.1109/JSTARS.2018.2835823.
   Web of Science ®Google Scholar
• Li, K. and Chen, Y. 2018. A Genetic Algorithm-based urban cluster automatic threshold method by combining VIIRS DNB, NDVI, and NDBI to monitor urbanization. Remote Sensing, 10 (2), 277. doi:10.3390/rs10020277.
   Web of Science ®Google Scholar
• Lillesand, T., Kiefer, R.W., and Chipman, J., 2015. Remote sensing and image interpretation. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Liu, S. et al., 2017. A novel semisupervised framework for multiple change detection in hyperspectral images. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA. 173–176. doi:10.1109/IGARSS.2017.8126922.
   Google Scholar
• Ma, X., et al., 2019. A new fusion approach for extracting urban built-up areas from multisource remotely sensed data. Remote Sensing, 11 (21), 2516. doi:10.3390/rs11212516.
   Web of Science ®Google Scholar
• Mahesh, B., 2020. Machine learning algorithms-a review. International Journal of Science and Research (IJSR) [Internet], 9 (1), 381–386.
   Google Scholar
• Main-Knorn, M., et al., 2017. Sen2Cor for sentinel-2. Image and Signal Processing for Remote Sensing XXIII, 10427, 37–48.
   Google Scholar
• Makineci, H.B. 2023. Spatio-temporal change detection of built-up areas with Sentinel-1 SAR data using random forest classification for Arnavutköy Istanbul. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 12 (2), 626–636. doi:10.28948/ngumuh.1203301.
   Google Scholar
• Maqsoom, A., et al., 2022. Extracting built-up areas from spectro-textural information using machine learning. Soft Computing, 26 (16), 7789–7808. doi:10.1007/s00500-022-06794-6.
   Web of Science ®Google Scholar
• Martinis, S., Plank, S., and Ćwik, K. 2018. The use of Sentinel-1 time-series data to improve flood monitoring in arid areas. Remote Sensing, 10 (4), 583. doi:10.3390/rs10040583.
   Web of Science ®Google Scholar
• Maxwell, A.E., Warner, T.A., and Fang, F. 2018. Implementation of machine-learning classification in remote sensing: An applied review. International Journal of Remote Sensing, 39 (9), 2784–2817. doi:10.1080/01431161.2018.1433343.
   Web of Science ®Google Scholar
• Misra, M., Kumar, D., and Shekhar, S., 2020. Assessing machine learning based supervised classifiers for built-up impervious surface area extraction from Sentinel-2 images. Urban Forestry & Urban Greening, 53, 126714. doi:10.1016/j.ufug.2020.126714
   Web of Science ®Google Scholar
• Otair, D.M., 2013. Approximate k-nearest neighbour based spatial clustering using kd tree. arXiv preprint arXiv:1303.1951. doi:10.5121/ijdms.2013.5108.
   Google Scholar
• Ouma, Y., et al., 2022. Comparison of machine learning classifiers for multitemporal and multisensor mapping of urban LULC features. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43, 681–689. doi:10.5194/isprs-archives-XLIII-B3-2022-681-2022
   Google Scholar
• Pang, G., Jin, H., and Jiang, S. 2015. CenKNN: a scalable and effective text classifier. Data Mining and Knowledge Discovery, 29 (3), 593–625. doi:10.1007/s10618-014-0358-x.
   Web of Science ®Google Scholar
• Pomente, A., Picchiani, M., and Del Frate, F., 2018. Sentinel-2 change detection based on deep features. IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain. 6859–6862. doi:10.1109/IGARSS.2018.8519195.
   Google Scholar
• Rouibah, K. and Belabbas, M. 2020. Applying multi-index approach from Sentinel-2 imagery to extract urban area in dry season (Semi-Arid Land in North East Algeria). Revista de Teledetección, 56 (56), 89–101. doi:10.4995/raet.2020.13787.
   Google Scholar
• Roy, D.P., et al., 2016. Best practices for the reprojection and resampling of Sentinel-2 multi spectral instrument level 1C data. Remote Sensing Letters, 7 (11), 1023–1032. doi:10.1080/2150704X.2016.1212419.
   Web of Science ®Google Scholar
• Rwanga, S.S. and Ndambuki, J.M. 2017. Accuracy assessment of land use/land cover classification using remote sensing and GIS. International Journal of Geosciences, 8 (4), 611. doi:10.4236/ijg.2017.84033.
   Google Scholar
• Samadzadegan, F., Toosi, A., and Javan, F.D. 2023. Automatic built-up area extraction by feature-level fusion of Luojia 1–01 nighttime light and Sentinel satellite imageries in Google Earth Engine. Advances in Space Research, 72 (4), 1052–1069. doi:10.1016/j.asr.2023.05.015.
   Web of Science ®Google Scholar
• Schulz, D., et al., 2021. Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS Journal of Photogrammetry and Remote Sensing, 178, 97–111. doi:10.1016/j.isprsjprs.2021.06.005
   Web of Science ®Google Scholar
• Shrestha, B., Stephen, H., and Ahmad, S. 2021. Impervious surfaces mapping at city scale by fusion of radar and optical data through a random forest classifier. Remote Sensing, 13 (15), 3040. doi:10.3390/rs13153040.
   Web of Science ®Google Scholar
• Sobie, C., Freitas, C., and Nicolai, M., 2018. Simulation-driven machine learning: bearing fault classification. Mechanical Systems and Signal Processing, 99, 403–419. doi:10.1016/j.ymssp.2017.06.025
   Web of Science ®Google Scholar
• Sonka, M. et al., 1993. Image pre-processing. Image Processing, Analysis and Machine Vision, 56–111. doi:10.1007/978-1-4899-3216-7_4.
   Google Scholar
• Spence, M., Annez, P.C., and Buckley, R.M., 2008. Urbanization and growth. Washington, DC: World Bank Publications.
   Google Scholar
• Story, M. and Congalton, R.G., 1986. Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing, 52 (3), 397–399.
   Web of Science ®Google Scholar
• Sun, L., et al., 2019. A machine learning-based classification system for urban built-up areas using multiple classifiers and data sources. Remote Sensing, 12 (1), 91. doi:10.3390/rs12010091.
   Web of Science ®Google Scholar
• Taha, L.G. and Ibrahim, R.E., 2021. Land use land cover mapping from Sentinel-1, Sentinel-2 and fused Sentinel images based on machine learning algorithms. International Journal of Applied Mathematics and Computer Science, 1, 12–23.
   Google Scholar
• Tavares, P.A., et al., 2019. Integration of Sentinel-1 and Sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors, 19 (5), 1140. doi:10.3390/s19051140.
   PubMed Web of Science ®Google Scholar
• Vigneshwaran, S. and Kumar, S.V., 2018. Extraction of built-up area using high resolution Sentinel-2a and Google satellite imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-4/W9, 165–169. doi:10.5194/isprs-archives-XLII-4-W9-165-2018
   Google Scholar
• Wahbi, M., et al., 2023. A deep learning classification approach using high spatial satellite images for detection of built-up areas in rural zones: case study of Souss-Massa region-Morocco. Remote Sensing Applications: Society & Environment, 29, 100898. doi:10.1016/j.rsase.2022.100898
   Google Scholar
• Wang, X., et al., 2023. Hcindex: a Hilbert-Curve-based clustering index for efficient multi-dimensional queries for cloud storage systems. Cluster Computing, 26 (3), 2011–2025. doi:10.1007/s10586-022-03723-y.
   Web of Science ®Google Scholar
• Whyte, A., Ferentinos, K.P., and Petropoulos, G.P., 2018. A new synergistic approach for monitoring wetlands using Sentinels-1 and 2 data with object-based machine learning algorithms. Environmental Modelling & Software, 104, 40–54. doi:10.1016/j.envsoft.2018.01.023
   Web of Science ®Google Scholar
• Wu, W., et al., 2023. CroFuseNet: a semantic segmentation network for urban impervious surface extraction based on cross fusion of optical and SAR images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 16, 2573–2588. doi:10.1109/JSTARS.2023.3250461
   Web of Science ®Google Scholar
• Xu, R., Liu, J., and Xu, J. 2018. Extraction of high-precision urban impervious surfaces from Sentinel-2 multispectral imagery via modified linear spectral mixture analysis. Sensors, 18 (9), 2873. doi:10.3390/s18092873.
   PubMed Web of Science ®Google Scholar