An Unsupervised Method of Extracting Constructions from Color Remote Sensed Image Based on Mean Shift and Neutrosophic Set
YU Bo1, 2, NIU Zheng1*, WANG Li1, LIU Ya-qi3, CHEN Fang4
1. The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3. Beihang University, Beijing 100191, China 4. Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Abstract：The diversity of spectral and textural information of constructions has become an obstacle in automatically detecting buildings. To overcome that, a method to detect constructions based on remote sensed images is proposed. It synthesizes neutrosophic set and mean shift algorithms to segment images transformed to neutrosophic set domain. After segmentation, an image is generated whose pixels mainly describe spectral categories based on main land features. Constructions can be extracted by generated spectral information. The algorithm overcomes the shortcomings of low stability, spectral discontinuity and complicated spectral information by enhancing and segmenting image in neutrosophic set domain. It avoids operations of extracting connected area before recognizing land features as well. Experiments show that the proposed algorithm can not only extract entire constructions steadily, precisely, completely and simply but also satisfies the demand of extracting constructions from high resolution remote sensing images.
于 博1, 2，牛 铮1*，王 力1，刘亚奇3，陈 方4 . 一种基于中性集和均值漂移的彩色遥感图像非监督建筑物提取方法 [J]. 光谱学与光谱分析, 2013, 33(04): 1071-1075.
YU Bo1, 2, NIU Zheng1*, WANG Li1, LIU Ya-qi3, CHEN Fang4 . An Unsupervised Method of Extracting Constructions from Color Remote Sensed Image Based on Mean Shift and Neutrosophic Set . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2013, 33(04): 1071-1075.
[1] Fukunaga F, Hostetler L D. IEEE Transactions on Information Theory, 1975, 21(1): 32. [2] Zheng L, Zhang J, Wang Q. Computers and Electronics in Agriculture, 2009, 65: 93. [3] SHEN Zhan-feng, LUO Jian-cheng, HU Xiao-dong, et al(沈占锋，骆剑承，胡晓东, 等). Geomatics and Information Science of Wuhan University(武汉大学学报·信息科学版), 2010, (3): 313. [4] Smarandache F. Neutrosophic Probability. American Research Press, 2005. [5] Zhang M, Zhang L, Cheng H. Signal Processing, 2010, 90(5): 1510. [6] Guo Y, Cheng H D. Pattern Recognition, 2009, 42(5): 587. [7] Sengur A, Guo Y. Computer Vision and Image Understanding, 2011, 115(8): 1134. [8] Kraipeerapun P, Fung C C. Neurocomputing, 2009, 72(13-15): 2845. [9] Xie X L, Beni G. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1991, 13(8): 841. [10] LIU Wen-ping, CHEN Wei-jun, WU Li-de(刘文萍, 陈维军, 吴立德). J. Infrared Millim. Waves(红外与毫米波学报)，1999, 18(1): 75. [11] Comaniciu D, Ramesh V, Meer P. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(5): 564. [12] LIN Hui, MO Deng-kui, XIONG Yu-jiu, et al(林 辉, 莫登奎, 熊育久, 等). Journal of Central South Forestry University(中南林学院学报), 2006, 26(4): 85. [13] Li B C, Guo Z G, Wen C. Fuzzy Systems and Mathematics, 2000, 14(4): 77.
