Object-based image analysis of suburban landscapes using Landsat-8 imagery

References

• An, Kai, Jinshui Zhang, and Yu Xiao. 2007. “Object-oriented Urban Dynamic Monitoring—A Case Study of Haidian District of Beijing.” Chinese Geographical Science 17 (3): 236–242. doi:10.1007/s11769-007-0236-1.
   Web of Science ®Google Scholar
• Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): Elsevier B.V.: 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
   Web of Science ®Google Scholar
• Blaschke, T., C. Burnett, and A. Pekkarinen. 2004. New Contextual Approaches Using Image Segmentation for Object-Based Classification. Dordrecht: Kluver Academic Publishers.
   Google Scholar
• Bo, Shukui, and Ling Ding. 2010. “The Effect of the Size of Training Sample on Classification Accuracy in Object-Oriented Image Analysis.” Journal of Image and Graphics 15: 1106–1111. (in Chinese) doi:10.11834/jig.20100708.
   Google Scholar
• Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Breiman, L. I., Jerome H. Friedman, R. A. Olshen, and C. J. Stone. 1984. “Classification and Regression Trees (CART).” Biometrics 40: 358. doi:10.2307/2530946.
   Google Scholar
• Burges, C. J. C. 1998. “A Tutorial on Support Vector Machines for Pattern Recognition.” Data Mining and Knowledge Discovery 2: 121–167. doi:10.1023/A:1009715923555.
   Web of Science ®Google Scholar
• Clinton, Nicholas, Ashley Holt, James Scarborough, Li Yan, and Peng Gong. 2010. “Accuracy Assessment Measures for Object-Based Image Segmentation Goodness.” Photogrammetric Engineering & Remote Sensing 76 (3): 289–299. doi:10.14358/PERS.76.3.289.
   Web of Science ®Google Scholar
• Congalton, Russell G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Congalton, Russell G., and Roy A. Mead. 1986. “A Review of Three Discrete Multivariate Analysis Techniques Used in Assessing the Accuracy of Remotely Sensed Data From Error Matrices.” IEEE Transactions on Geoscience and Remote Sensing GE-24 (1): 169–174. doi:10.1109/TGRS.1986.289546.
   Web of Science ®Google Scholar
• Cortes, Corinna, and Vladimir Vapnik. 1995. “Support-Vector Networks.” Machine Learning 20 (3): 273–297. doi:10.1023/A:1022627411411.
   Web of Science ®Google Scholar
• Duro, Dennis C., Steven E. Franklin, and Monique G. Dubé. 2012. “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118 ( Elsevier Inc): 259–272. doi:10.1016/j.rse.2011.11.020.
   Web of Science ®Google Scholar
• Estoque, Ronald C, Yuji Murayama, and Chiaki Mizutani Akiyama. 2015. “Pixel-Based and Object-Based Classifications Using High- and Medium-Spatial-Resolution Imageries in the Urban and Suburban Landscapes.” Geocarto International 30 (10): 1113–1129. doi:10.1080/10106049.2015.1027291.
   Web of Science ®Google Scholar
• Faris, Hossam, Mohammad A. Hassonah, Ala’ M. Al-Zoubi, Seyedali Mirjalili, and Ibrahim Aljarah. 2017. “A Multi-Verse Optimizer Approach for Feature Selection and Optimizing SVM Parameters Based on a Robust System Architecture.” Neural Computing and Applications no. January. Springer London: 1–15. doi:10.1007/s00521-016-2818-2.
   Web of Science ®Google Scholar
• Good, I. J. 1966. “The Estimation of Probabilities: an Essay on Modern Bayesian Methods.” Biometrics 23 (1): 158–161. doi:10.2307/2528296.
   Web of Science ®Google Scholar
• Hall, M. A., and Geoffrey Holmes. 2003. “Benchmarking Attribute Selection Techniques for Discrete Class Data Mining.” IEEE Transactions on Knowledge and Data Engineering 15 (6): 1437–1447. doi:10.1109/TKDE.2003.1245283.
   Web of Science ®Google Scholar
• Haralick, Robert M., K. Shanmugam, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on System, Man and Cybernetics SMC-3 (6): 610–621. doi: 10.1109/TSMC.1973.4309314
   Web of Science ®Google Scholar
• Huang, Chengquan, L. S. Davis, and J. R. G. Townshen. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23 (4): 725–749. doi:10.1080/01431160110040323.
   Web of Science ®Google Scholar
• Joshi, Neha, Matthias Baumann, Andrea Ehammer, Rasmus Fensholt, Kenneth Grogan, Patrick Hostert, Martin Rudbeck Jepsen, et al. 2016. “A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring.” Remote Sensing 8 (1): 1–23. doi:10.3390/rs8010070.
   Web of Science ®Google Scholar
• Kartikeyan, B., A. Sarkar, and K. L. Majumder. 1998. “A Segmentation Approach to Classification of Remote Sensing Imagery.” International Journal of Remote Sensing 19 (9): 1695–1709. doi:10.1080/014311698215199.
   Web of Science ®Google Scholar
• Kaszta, Zaneta, Ruben Van De Kerchove, Abel Ramoelo, Moses Azong Cho, Sabelo Madonsela, Renaud Mathieu, and Eléonore Wolff. 2016. “Seasonal Separation of African Savanna Components Using WorldView-2 Imagery: A Comparison of Pixeland Object-Based Approaches and Selected Classification Algorithms.” Remote Sensing 8 (9): 763. doi:10.3390/rs8090763.
   Web of Science ®Google Scholar
• Li, Manchun, Lei Ma, Thomas Blaschke, Liang Cheng, and Dirk Tiede. 2016. “A Systematic Comparison of Different Object-Based Classification Techniques Using High Spatial Resolution Imagery in Agricultural Environments.” International Journal of Applied Earth Observation and Geoinformation 49: 87–98. doi:10.1016/j.jag.2016.01.011.
   Web of Science ®Google Scholar
• Li, Congcong, Jie Wang, Lei Wang, Luanyun Hu, and Peng Gong. 2014. “Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery.” Remote Sensing 6 (2): 964–983. doi:10.3390/rs6020964.
   Web of Science ®Google Scholar
• Ma, Lei, Liang Cheng, Wenquan Han, Lishan Zhong, and Manchun Li. 2014. “Cultivated Land Information Extraction from High-Resolution Unmanned Aerial Vehicle Imagery Data.” Journal of Applied Remote Sensing 8 (1): 083673. doi:10.1117/1.JRS.8.083673.
   Google Scholar
• Ma, Lei, Liang Cheng, Manchun Li, Yongxue Liu, and Xiaoxue Ma. 2015. “Training Set Size, Scale, and Features in Geographic Object-Based Image Analysis of Very High Resolution Unmanned Aerial Vehicle Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 102: International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS): 14–27. doi: 10.1016/j.isprsjprs.2014.12.026
   Web of Science ®Google Scholar
• Ma, Lei, Tengyu Fu, Thomas Blaschke, Manchun Li, Dirk Tiede, Zhenjin Zhou, Xiaoxue Ma, and Deliang Chen. 2017a. “Evaluation of Feature Selection Methods for Object-Based Land Cover Mapping of Unmanned Aerial Vehicle Imagery Using Random Forest and Support Vector Machine Classifiers.” ISPRS International Journal of Geo-Information 6 (2): 51. doi:10.3390/ijgi6020051.
   Web of Science ®Google Scholar
• Ma, Lei, Manchun Li, Xiaoxue Ma, Liang Cheng, Peijun Du, and Yongxue Liu. 2017b. “A Review of Supervised Object-Based Land-Cover Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 277–293. doi:10.1016/j.isprsjprs.2017.06.001.
   Web of Science ®Google Scholar
• Mather, Paul M., and Magaly Koch. 2011. Computer Processing of Rmeotely-Sensed Images. 4th ed. Chichester: Wiley-Blackwell.
   Google Scholar
• Myint, Soe W., Patricia Gober, Anthony Brazel, Susanne Grossman-Clarke, and Qihao Weng. 2011. “Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery.” Remote Sensing of Environment 115 (5): Elsevier Inc.: 1145–1161. doi:10.1016/j.rse.2010.12.017.
   Web of Science ®Google Scholar
• Naghibi, Seyed Amir, Kourosh Ahmadi, and Alireza Daneshi. 2017. “Application of Support Vector Machine, Random Forest, and Genetic Algorithm Optimized Random Forest Models in Groundwater Potential Mapping.” Water Resources Management 31 (9): 2761–2775. doi:10.1007/s11269-017-1660-3.
   Web of Science ®Google Scholar
• Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
   Web of Science ®Google Scholar
• Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48: 2297–2307. doi:10.1109/TGRS.2009.2039484.
   Web of Science ®Google Scholar
• Pedergnana, Mattia, Prashanth Reddy Marpu, Mauro Dalla Mura, Jón Atli Benediktsson, and Lorenzo Bruzzone. 2013. “A Novel Technique for Optimal Feature Selection in Attribute Profiles Based on Genetic Algorithms.” IEEE Transactions on Geoscience and Remote Sensing 51 (6): 3514–3528. doi: 10.1109/TGRS.2012.2224874
   Web of Science ®Google Scholar
• Petropoulos, George P., Charalambos Kontoes, and Iphigenia Keramitsoglou. 2011. “Burnt Area Delineation From a Uni-Temporal Perspective Based on Landsat TM Imagery Classification Using Support Vector Machines.” International Journal of Applied Earth Observation and Geoinformation 13 (1): Elsevier B.V.: 70–80. doi:10.1016/j.jag.2010.06.008.
   Web of Science ®Google Scholar
• Piper, J. 1987. “The Effect of Zero Feature Correlation Assumption on Maximum Likelihood Based Classification of Chromosomes.” Signal Processing 12: 49–57. doi:10.1016/0165-1684(87)90081-8.
   Web of Science ®Google Scholar
• Platt, Rutherford V, and Lauren Rapoza. 2008. “An Evaluation of an Object- Oriented Paradigm for Land Use/Land Cover Classification an Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification.” The Professional Geographer 60: 87–100. doi:10.1080/00330120701724152.
   Web of Science ®Google Scholar
• Pradhan Biswajeet. 2013. “A Comparative Study on the Predictive Ability of the Decision Tree, Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS.” Computer and Geosciences 51: 350–365. doi:10.1016/j.cageo.2012.08.023.
   Google Scholar
• Pu, Ruiliang, Shawn Landry, and Qian Yu. 2011. “Object-Based Urban Detailed Land Cover Classification with High Spatial Resolution IKONOS Imagery.” International Journal of Remote Sensing 32 (12): 3285–3308. doi:10.1080/01431161003745657.
   Web of Science ®Google Scholar
• Qian, Yuguo, Weiqi Zhou, Jingli Yan, Weifeng Li, and Lijian Han. 2015. “Comparing Machine Learning Classifiers for Object-Based Land Cover Classification Using Very High Resolution Imagery.” Remote Sensing 7 (1): 153–168. doi:10.3390/rs70100153.
   Web of Science ®Google Scholar
• Rish, Irina. 2001. “An Empirical Study of the Naive Bayes Classifier.” IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence 3 (January 2001): 41–46.
   Google Scholar
• Rodriguez-Galiano, V. F., B. Ghimire, J. Rogan, M. Chica-Olmo, and J. P. Rigol-Sanchez. 2012. “An Assessment of the Effectiveness of a Random Forest Classifier for Land-Cover Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 67: 93–104. doi:10.1016/j.isprsjprs.2011.11.002.
   Web of Science ®Google Scholar
• Song, Xianfeng, Zheng Duan, and Xiaoguang Jiang. 2012. “Comparison of Artificial Neural Networks and Support Vector Machine Classifiers for Land Cover Classification in Northern China Using a SPOT-5 HRG Image.” International Journal of Remote Sensing 33 (10): 3301–3320. doi: 10.1080/01431161.2011.568531
   Web of Science ®Google Scholar
• Trimble Documentation eCognition® Developer 9.0 Reference Book. 2014. München, Germany.
   Google Scholar
• Van Niel, Thomas G., Tim R. McVicar, and Bisun Datt. 2005. “On the Relationship between Training Sample Size and Data Dimensionality: Monte Carlo Analysis of Broadband Multi-Temporal Classification.” Remote Sensing of Environment 98 (4): 468–480. doi:10.1016/j.rse.2005.08.011.
   Web of Science ®Google Scholar
• Wieland, Marc, and Massimiliano Pittore. 2014. “Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-Spectral Satellite Images.” Remote Sensing 6 (4): 2912–2939. doi:10.3390/rs6042912.
   Web of Science ®Google Scholar
• Wieland, Marc, Yolanda Torres, Massimiliano Pittore, and Belen Benito. 2016. “Object-Based Urban Structure Type Pattern Recognition from Landsat TM with a Support Vector Machine.” International Journal of Remote Sensing 37 (17): 4059–4083. doi:10.1080/01431161.2016.1207261.
   Web of Science ®Google Scholar
• Wu, Hao, Zhiping Cheng, Wenzhong Shi, Zelang Miao, and Chenchen Xu. 2014a. “An Object-Based Image Analysis for Building Seismic Vulnerability Assessment Using High-Resolution Remote Sensing Imagery.” Natural Hazards 71 (1): 151–174. doi:10.1007/s11069-013-0905-6.
   Web of Science ®Google Scholar
• Wu, Hao, Lu-Ping Ye, Wen-Zhong Shi, and Keith C. Clarke. 2014b. “Assessing the Effects of Land Use Spatial Structure on Urban Heat Islands Using HJ-1B Remote Sensing Imagery in Wuhan, China.” International Journal of Applied Earth Observation and Geoinformation 32 (October). Elsevier B.V.: 67–78. doi:10.1016/j.jag.2014.03.019.
   Google Scholar
• Yan, Gao J. F. Mas, B. H. P. Maathuis, X. Zhang, and P. M. G. Van Dijk. 2006. “Comparison of Pixel-Based and Object-Oriented Image Classification Approaches—A Case Study in a Coal Fire Area, Wuda, Inner Mongolia, China.” International Journal of Remote Sensing 27 (18): 4039–4055. doi: 10.1080/01431160600702632
   Web of Science ®Google Scholar
• Yu, Qian, Peng Gong, Nick Clinton, Greg Biging, Maggi Kelly, and Dave Schirokauer. 2006. “Object-Based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery.” Photogrammetric Engineering & Remote Sensing 72 (7): 799–811. doi:10.14358/PERS.72.7.799.
   Web of Science ®Google Scholar
• Zhang, Min Ling, José M. Peña, and Victor Robles. 2009. “Feature Selection for Multi-Label Naive Bayes Classification.” Information Sciences 179 (19): 3218–3229. doi:10.1016/j.ins.2009.06.010.
   Web of Science ®Google Scholar