مقایسه کارآیی توابع پیکسل پایة الگوریتم ماشین‌بردار پشتیبان و عملگرهای فازی شئ‌گرا در استخراج اراضی ساخته شده شهری همدان

 Akbari, E., & Shekari Badi, A. (2014). Processing and extracting information from satellite data using ENVI software. Teran:  Mahvareh press. (In Persian)

Arekhi, S.,  & Adibnejad, M. (2012). Efficiency assessment of the of Support Vector Machines for land use classification using Landsat ETM+ data (Case study: Ilam Dam Catchment). Iranian Journal of Range and Desert Research, 18(3), 420- 440. (In Persian)

Azhdari, A., Taghvaee, A. A., & Kheyroddin, R. (2018). Spatiotemporal analysis of Shiraz metropolitan area expansion during 1986–2014: using remote sensing imagery and landscape metrics. Int J Architect Eng Urban Plan, 28(2), 163-173. https://doi.org/10.22068/ijaup.28.2.163

Behnia, N., Zare, M., Moosavi, V., & Khajeddin, S. I. (2020). Evaluation of a Hierarchical Classification Method and Statistical Comparison with Pixel-Based and Object-Oriented Approaches. Ecopersia, 8(4), 209-219. http://ecopersia.modares.ac.ir/article-24-38774-en.html

Berila, A., & Isufi, F. (2021). Two Decades (2000–2020) Measuring Urban Sprawl Using GIS, RS and Landscape Metrics: a Case Study of Municipality of Prishtina (Kosovo) [journal article]. Journal of Ecological Engineering, 22(6), 114-125. https://doi.org/10.12911/22998993/137078

Dang, K. B., Nguyen, T. H. T., Nguyen, H. D., Truong, Q. H., Vu, T. P., Pham, H. N., Duong, T. T., Giang, V. T., Nguyen, D. M., Bui, T. H., & Burkhard, B. (2022). U-shaped deep-learning models for island ecosystem type classification, a case study in Con Dao Island of Vietnam. One Ecosystem, 7. https://doi.org/10.3897/oneeco.7.e79160

Fallatah, A., Jones, S., Wallace, L., & Mitchell, D. (2022). Combining Object-Based Machine Learning with Long-Term Time-Series Analysis for Informal Settlement Identification. Remote Sensing, 14(5), 1226. https://www.mdpi.com/2072-4292/14/5/1226

Feizizadeh,  B., Pirnazar,  M., Zand Karimi, A., Abedi Gheshlaghi, H., (2015). The effect of different classification algorithms knowledge base in the increase the accuracy of the maps of land use. Journal of of Geographical Data (SEPEHR), 24(94), 107-117. (In Persian)

Feizizadeh, B. (2018). A Novel Approach of Fuzzy Dempster–Shafer Theory for Spatial Uncertainty Analysis and Accuracy Assessment of Object-Based Image Classification. IEEE Geoscience and Remote Sensing Letters, 15(1), 18-22. https://doi.org/10.1109/LGRS.2017.2763979

Feizizadeh, B., Blaschke, T., Tiede, D., & Moghaddam, M. H. R. (2017). Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology, 293, 240-254. https://doi.org/10.1016/j.geomorph.2017.06.002

Fringe. Proceedings of the 12th Remote Sensing Conference.16 -21 April 2005. Goiania, Brazilian.

Furberg, D. Ban, Y.and Nascetti, A. (2019). Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data. Journal of Remote Sensing. 11(20), 2408-2442.

Ghasem Torkashvand, M., & Mousapour, M. (2021). Evaluate the efficiency of kernel functions of vector support machine and object-oriented fuzzy operators in estimating the level of snow cover using Sentinel-2B satellite data - Case study: Almabolagh Mountain. Journal of of Geographical Data (SEPEHR), 30(119), 175-187. https://doi.org/10.22131/sepehr.2021.247893 (In Persian)

Ghobadeyan, Z., Alikhah Asl, M., Rezvani, M.  (2021). Investigating the Effects of Urban Development on Rangelands and Forests of Sirvan City Using Remote Sensing 1987-2016. Journal of Urban Ecology Researches, 11(21), 107-120. https://doi.org/10.30473/grup.2020.7475 (In Persian)

Hartoni, H., Siregar, V., Wouthuyzen, S., & Agus, S. (2022). Object based classification of benthic habitat using Sentinel 2 imagery by applying with support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia. Biodiversitas Journal of Biological Diversity, 23. https://doi.org/10.13057/biodiv/d230155

He, Z., Zhao, C., Fürst, C., & Hersperger, A. M. (2021). Closer to causality: How effective is spatial planning in governing built-up land expansion in Fujian Province, China? Land Use Policy, 108, 105562. https://doi.org/https://doi.org/10.1016/j.landusepol.2021.105562

Herold, M., Scepan, J., & Clarke, K. C. (2002). The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses. Environment and Planning A: Economy and Space, 34(8), 1443-1458. https://doi.org/10.1068/a3496

Jafarzadeh, J., & Nazmfar, H. (2019). Classification of Satellite Images in the Evaluation of Urban Land Use Change Using Scale Optimization in Objected Oriented Processing (Case Study: Ardabil City). Research and Urban Planning, 10(36), 117-128. (In Persian)

Javandel, P., Hassani, H., & Maghsodi, A. (2018). Comparison of support vector machines and spectral angle mapper algorithms for Alteration mapping in Pariz area, Kerman province. Journal of Mineral Resources Engineering, 2(4), 1-10. (In Persian)

Jiang, H., Sun, Z., Guo, H., Xing, Q., Du, W., & Cai, G. (2022). A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990–2015. Big Earth Data, 6(1), 103-126. https://doi.org/10.1080/20964471.2021.1950351

Kazemi Garajeh, M., Feizizadeh, B., Weng, Q., Hossein, M., Rezaei Moghaddam, M. H., & Garajeh, A. (2022). Desert landform detection and mapping using a semi-automated object-based image analysis approach. Journal of Arid Environments, 199, 104721. https://doi.org/10.1016/j.jaridenv.2022.104721

Keshtkar, H., Voigt, W., & Alizadeh, E. (2017). Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arabian Journal of Geosciences, 10(6), 154. https://doi.org/10.1007/s12517-017-2899-y

Li, C., Wang, X., Wu, Z., Dai, Z., Yin, J., & Zhang, C. (2021). An Improved Method for Urban Built-Up Area Extraction Supported by Multi-Source Data. Sustainability, 13(9), 5042. https://www.mdpi.com/2071-1050/13/9/5042

Mohammadi, M., & Sarmad, M. (2019). Robustified distance based fuzzy membership function for support vector machine classification. Iranian Journal of Fuzzy Systems, 16(6), 191-204. https://doi.org/10.22111/ijfs.2019.5028

Mousipour, M., Hosseini, S.A., & Safai, Sh. (2018). Evaluation of the impact of meteorological drought phenomenon on the vegetation of Hamadan city Using remote sensing and geographic information system. National Conference on Water Crisis and Ways to Get Out,  Payame Noor University, Center Kabodarahang, 11 May 2018. (In Persian)

Najafi, P., Navid, H., Feizizadeh, B., Eskandari, I., & Blaschke, T. (2019). Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue. Remote Sensing, 11(21), 2583. https://www.mdpi.com/2072-4292/11/21/2583

Ojaghi, s., &  khazai, s. (2018). Improving the Detection of Object-Oriented Changes in High-Resolution Images based on Random Forest Method in Optimal Features Space. Journal of Geographical Data (SEPEHR), 26(104), 117-127. (In Persian)

Pishnamaz Ahmadi, M.,  Mohammadzadeh, K., & Hejazi, S.A. (2018). Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County). Geography and Sustainability of Environment, 7(25), 95-111. (In Persian)

Polat, A. B., Akcay, O., & Balik Sanli, F. (2022). Monitoring seasonal effects in vegetation areas with Sentinel-1 SAR and Sentinel-2 optic satellite images. Arabian Journal of Geosciences, 15(7), 670. https://doi.org/10.1007/s12517-022-09947-x

Rezaei Moghaddam, M.R.,  Valizadeh Kamran, Kh., &  Andaryani, S.,   Almaspoor, F. (2014). Comparison of ANN and SVM methods in extraction Land Use/ Land Cover maps from Landsat 8 satellite image (Case Study: Sufi Chay Basin). Journal of Geography and Planning (JGP), 19(52), 163-183. (In Persian)

Roostaei, Sh., Nikjo, M. R., Valizadeh Kamran, Kh., Alavi, S. A. (2010). Evaluation of the neural network method and object-oriented classification in the extraction of urban area changes (case study of Tabriz city). National Conference on Geographical Space, Landscaping Approach, Environmental Management Holder, Islamic Azad University of Eslamshahr, 21 November 2010. (In Persian)

Sabo, F., Corbane, C., Florczyk, A. J., Ferri, S., Pesaresi, M., & Kemper, T. (2018). Comparison of built-up area maps produced within the global human settlement framework. Transactions in GIS, 22(6), 1406-1436. https://doi.org/https://doi.org/10.1111/tgis.12480

Šiljeg, A., Panđa, L., Domazetović, F., Marić, I., Gašparović, M., Borisov, M., & Milošević, R. (2022). Comparative Assessment of Pixel and Object-Based Approaches for Mapping of Olive Tree Crowns Based on UAV Multispectral Imagery. Remote Sensing, 14(3), 757. https://www.mdpi.com/2072-4292/14/3/757

Sivakumar, V. (2014). UrbanMapping and Growth Prediction Using Remote Sensing and GIS Techniques, Pune, India. Proceedings of the 8th The International of the Photogrammetry, Remote Sensing and Spatial Information Sciences Conference.09 -12 December 2014. Hyderabad, India.

Valdiviezo-N, J. C., Téllez-Quiñones, A., Salazar-Garibay, A., & López-Caloca, A. A. (2018). Built-up index methods and their applications for urban extraction from Sentinel 2A satellite data: discussion. Journal of the Optical Society of America. 35(1), 35-44. https://doi.org/10.1364/JOSAA.35.000035

Wang, X., Liu, S., Du, P., Liang, H., Xia, J., & Li, Y. (2018). Object Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning. Journal of Remote Sensing, 10(02), 276 - 289. https://doi.org/10.3390/rs10020276

Wen, C., Lu, M., Bi, Y., Zhang, S., Xue, B., Zhang, M., Zhou, Q., & Wu, W. (2022). An Object-Based Genetic Programming Approach for Cropland Field Extraction. Remote Sensing, 14(5), 1275. https://www.mdpi.com/2072-4292/14/5/1275  Workie, T. G., & Hailu, T. M. (2021). Object-Based Image Analysis for Land Cover Mapping in an Urbanized Watershed. Journal of Urban Development Studies, 1(2), 72-87. https://doi.org/10.1234/ecsujuds%y1272-87 

Wu, J., Lin, L., Zhang, C., Li, T., Cheng, X., & Nan, F. (2023). Generating Sentinel-2 all-band 10-m data by sharpening 20/60-m bands: A hierarchical fusion network. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 16-31. https://doi.org/https://doi.org/10.1016/j.isprsjprs.2022.12.017

Yongqiao, W., Shouyang, W., & Lai, K. K. (2005). A new fuzzy support vector machine to evaluate credit risk. IEEE Transactions on Fuzzy Systems, 13(6), 820-831. https://doi.org/10.1109/TFUZZ.2005.859320  (In Persian)