ثروتی، محمدرضا؛ خضری، سعید؛ رحمانی، توفیق. (1388). بررسی تنگناهای طبیعی توسعۀ فیزیکی شهر سنندج، پژوهشهای جغرافیای طبیعی، دانشگاه تهران، شمارۀ 67، صص 29-13.
سازمان مدیریت و برنامهریزی استان ایلام. (1385). جایگاه استان ایلام در تحقق جهتگیریهای آمایش سرزمین. سالنامۀ آماری ایلام (جمعیت).
فردوسی، بهرام. (1384). امکانسنجی و کاربرد سیستم پشتیبانی تصمیمگیری در توسعۀ فیزیکی شهر (نمونۀ موردی: سنندج)، پایاننامۀ کارشناسی ارشد، تهران، دانشگاه تربیت مدرس.
فیضیزاده، بختیار. (1386). مقایسۀ روشهای پیکسل پایه و شیءگرا در تهیۀ نقشۀ کاربری اراضی، پایاننامۀ کارشناسی ارشد، مرکز دانشگاه تبریز، 103-97.
نجفی، اسماعیل. (1389). قابلیتها و محدویتهای ژئومورفولوژیکی توسعۀ فیزیکی شهر ایلام، پایاننامۀ کارشناسی ارشد ژئومورفولوژی، دانشگاه تهران.
Alavipanah, S.K. (2003). Application Remote Sensing in Geology (Earth Sciences), Tehran University Press, 478 pages.
Baatz, M., Schape, A. (1999). Object-oriented and Multi Scale Image Analysis in Semantic Networks, Proceeding of the 2nd international symposium on remote sensing,16-22 August, Ensched, ITC.
Baatz, M., Ursula, B., Seyed, D., Markus, H., Astrid, H., Peter, H., Iris, L., Matthias, M., Malte, S., Michaela, W., Gregor,W. (2004). eCognition User Guide, Definiens Imaging GmbH, München Germany.
Boniad, A.E., Hajighaderi, T. (2008). Mapping of Natural Forest Stands of Zanjan Province Using Landsat 7ETM+ sensor data, Science and Technology of Agriculture and Natural Resources, 42 (11): 627-638.
Borri, D., Caprioli, M., Tarantino, E. (2005). Spatial Information Extraction from VHR Satellite Data to Detect Land Cover Transformations, Polytechnic University of Bari, Italy, pp.105.
Chavez, P.S.J.R., Mackinnon, D.J. (1994). "Automatic detection of vegetation changes in the southwestern United States using remotely sensed images", Photogrammetric Engineering and Remote Sensing, 60: 571–583.
Chen, M., Su, W., Li, L., Chao, Z., Yue, A., Li, H. (2009). Pixel-based and Object-oriented Knowledgebased Classification Methods Using SPOT5 Imagery, WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS, ISSN: 1790-0832, pages 477-489.
Dehvari, A., Heck, R.J. (2009). Comparison of object-based and pixel based infrared airborne image classification methods using DEM thematic layer, Journal of Geography and Regional Planning, 2 (4): 086-096.
Du, Y., Teillet, P.M., Cihlar, J. (2002). "Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection", Remote Sensing of Environment, 82: 123–134.
FAO Soils Bulletin 32. (1981). A framework for land evaluation. FAO Publication, Rome,66p.
Fazizadeh, B. (2007). Comparison of pixel-based and object-oriented methods in land use mapping Master's thesis, GIS Center Tabriz University.
Fazizadeh, B., Helali, H. (2010). Comparison of pixel-based and object-oriented and parameters affecting the on land use/cover of West Azerbaijan province, Geography Studies, No. 71, 73-84.
Flanders, D., Hall-Beyer, M., Pereverzoff, J. (2003). Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction. Canadian Journal of Remote Sensing, 29, pp. 441-452.
Gao,Y., Mas, J.F., Navarrete, A. (2009). The improvement of an object-oriented classification using multi-temporal MODIS EVI satellite data, International Journal of Digital Earth, Volume 2, Issue 3 September 2009 , pp. 219 - 236
Hussaina, M., Chen, D., Cheng, A., Wei, H., Stenley, D. (2013). Change detection from remotely sensed images: From pixel-based to object-based approaches, ISPRS Journal of Photogrammetry and Remote Sensing 80: 91–106.
Karami, A., Khorani, A.A., Falahshamsi, S.R., Mosavi, V., Khosravi, G.H. (2012). Object-oriented application of remote sensing to map gully erosion, 20th Conference of Geomatics of Iran, 8 p.
Khosravi, I., Momeni, M. (2012). Identification structure of high-resolution satellite imagery using object-based image analysis, 20th Conference of Geomatics of Iran, 10 p.
Mackie, R.I. (2013). Dynamic analysis of structures on multicore computers – Achieving efficiency through object oriented design, Advances in Engineering Software 66: 3–9.
Matinfar, H.R., Sarmadian, F., Alavipanah, S.K., Heck, R. (2008). Characterizing Land use/land cover types by Landsat7data based upon Object oriented approach in Kashan region, Iranian journal of Range and Desert Reseach, 14 (4): 589-602.
Mori, M., Hirose, Y., Akamatsu, Y.L. (2003). Object- based classification of Ikonos data for rural land use mapping.
Http://www.define.com. eCognition Applied Notes , Vol 5 , No. 1.
Petropoulos, G.P., Kalaitzidis, C., Vadrevu, K.P. (2012). Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery, Computers & Geosciences, 41: 99–107.
Puissant, A., Rougier, S., Stumpf, A. (2014). Object-oriented mapping of urban trees using Random Forest classifiers, International Journal of Applied Earth Observation and Geoinformation, 26: 235–245.
Rasouli, A.A. (2008). Principles of remote sensing image processing applications, with emphasis on satellite, Tabriz University Press, 777 pages.
Schiewe, J. (2002). Segmentation of high-resolution remotely sensed data concepts, application and problems, in Symposium on geospatial theory, processing and applications, Ottawa, Canada, 235-242.
Wang, L., Sousa, W.P., Gong, P. (2004). Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery, International jornal of Remote sensing, 25 (24): 5655-5668.
Yaghobzadeh, M., Akbarpour, A. (2011). The effect of satellite image classification algorithm based on curve number runoff and maximum flood discharge using GIS and RS, Geography and Development 9 (22):5-22.
Yan, G . (2003). Pixel Based and Object Oriented Image for Coal Fire Research,
http://www.ITC.com (accessed in July 2008). pp. 3-99.
Yu, H.Y., Cheng, G., Ge, X.S., Lu, X.P. (2011). Object oriented land cover classification using ALS and GeoEye imagery over mining area, Transactions Nonferrous Metals Society of China 21:733-737.
Yu, Q., Gong, P., Clinton, N., Biging, G., Kelly, M., Schirokauer, D. (2006). Object-Based detailed vegetation classification with airborn high spatial resolution semote sensing imagery, hotogrammetric Engineering & Remote Sensing, 72 (7): 799-811.
Yue, A., Li, H. (2009). Pixel-based and object-oriented knowledge-based classification methods using SPOT5 Imagery, WSEAS Transactions on Information Science and Applications., ISSN: 1790-0832, pages 477-489.
Zhou, W., Troy, A., Grove, M. (2005). Measuring Urban parcel lawn Greenness by using an object-oriented classification approach, Rubenstein school of environment and natural Resources, University of Vermont, George D.Aiken Center, 81.