[1]吴诗婳,吴一全,周建江.直线截距直方图城区遥感图像多阈值分割[J].智能系统学报,2018,13(2):227-235.[doi:10.11992/tis.201609012]
 WU Shihua,WU Yiquan,ZHOU Jianjiang.Multi-level thresholding for remote sensing image of urban area based on line intercept histogram[J].CAAI Transactions on Intelligent Systems,2018,13(2):227-235.[doi:10.11992/tis.201609012]
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直线截距直方图城区遥感图像多阈值分割

参考文献/References:
[1] SIRMACEK B, UNSALAN C. Urban area detection using local feature points and spatial voting[J]. IEEE geoscience and remote sensing letters, 2010, 7(1): 146-150.
[2] SIRMA?EK B, üNSALAN C. Using local features to measure land development in urban regions[J]. Pattern recognition letters, 2010, 31(10): 1155-1159.
[3] 朱江洪, 李江风, 叶菁. 利用决策树工具的土地利用类型遥感识别方法研究[J]. 武汉大学学报: 信息科学版, 2011, 36(3): 301-305.
ZHU Jianghong, LI Jiangfeng, YE Jing. Land use information extraction from remote sensing data based on decision tree tool[J]. Geomatics and information science of Wuhan university, 2011, 36(3): 301-305.
[4] 陈洪, 陶超, 邹峥嵘, 等. 一种新的高分辨率遥感影像城区提取方法[J]. 武汉大学学报·信息科学版, 2013, 38(9): 1063-1067.
CHEN Hong, TAO Chao, ZOU Zhengrong, et al. Automatic urban area extraction using a Gabor filter and high-resolution remote sensing imagery[J]. Geomatics and information science of Wuhan university, 2013, 38(9): 1063-1067.
[5] 李丽, 柴文婷, 梅树立. 基于自适应全局阈值融合标记的遥感图像建筑群分割[J]. 农业机械学报, 2013, 44(7): 222-228.
LI Li, CHAI Wenting, MEI Shuli. Segmentation of remote sensing images based on adaptive global threshold and fused markers[J]. Transactions of the Chinese society for agricultural machinery, 2013, 44(7): 222-228.
[6] 陈琪, 熊博莅, 陆军, 等. 改进的二维Otsu图像分割方法及其快速实现[J]. 电子与信息学报, 2010, 32(5): 1100-1104.
CHEN Qi, XIONG Boli, LU Jun, et al. Improved Two-Dimensional Otsu image segmentation method and fast recursive realization[J]. Journal of electronics and information technology, 2010, 32(5): 1100-1104.
[7] RANJANI J J, THIRUVENGADAM S J. Fast threshold selection algorithm for segmentation of synthetic aperture radar images[J]. IET radar, sonar and navigation, 2012, 6(8): 788-795.
[8] 张金矿, 吴一全. 基于TZHANG Jinkuang, WU Yiquan. Image thresholding based on 2-D oblique exponent entropy method and Tent map chaotic particle swarm algorithm[J]. Signal processing, 2010, 26(5): 703-708.t映射CPSO的二维斜分指数熵阈值分割[J]. 信号处理, 2010, 26(5): 703-708.
ZHANG Jinkuang, WU Yiquan. Image thresholding based on 2-D oblique exponent entropy method and Tent map chaotic particle swarm algorithm[J]. Signal processing, 2010, 26(5): 703-708.
[9] SARKAR S, DAS S, CHAUDHURI S S. A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution[J]. Pattern recognition letters, 2015, 54: 27-35.
[10] MA?YSZKO D, STEPANIUK J. Adaptive multilevel rough entropy evolutionary thresholding[J]. Information sciences, 2010, 180(7): 1138-1158.
[11] NIAZMARDI S, NAEINI A A, HOMAYOUNI S, et al. Particle swarm optimization of kernel-based fuzzy C-means for hyperspectral data clustering[J]. Journal of applied remote sensing, 2012, 6(1): 063601.
[12] KAPUR J N, SAHOO P K, WONG A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer vision, graphics, and image processing, 1985, 29(3): 273-285.
[13] CAO L, SHI Z, CHENG E K W. Fast automatic multilevel thresholding method[J]. Electronics letters, 2002, 38(16): 868-870.
[14] 吴一全, 孟天亮, 吴诗婳, 等. 基于二维倒数灰度熵的河流遥感图像分割[J]. 华中科技大学学报: 自然科学版, 2014, 42(12): 70-74, 80.
WU Yiquan, MENG Tianliang, WU Shihua, et al. Remote sensing images segmentation of rivers based on two-dimensional reciprocal gray entropy[J]. Journal of Huazhong university of science and technology: nature science, 2014, 42(12): 70-74, 80.
[15] 陈恺, 陈芳, 戴敏, 等. 基于萤火虫算法的二维熵多阈值快速图像分割[J]. 光学精密工程, 2014, 22(2): 517-523.
CHEN Kai, CHEN Fang, DAI Min, et al. Fast image segmentation with multilevel threshold of two-dimensional entropy based on firefly algorithm[J]. Optics and precision engineering, 2014, 22(2): 517-523.
[16] 罗希平, 田捷. 用最大熵原则作多阈值选择的条件迭代算法[J]. 软件学报, 2000, 11(3): 379-385.
LUO Xiping, TIAN Jie. The ICM algorithm for multi-level threshold selection by maximum entropy criterion[J]. Journal of software, 2000, 11(3): 379-385.
[17] 郑毅, 郑苹. 结合模糊熵和遗传算法的双阈值图像分割[J]. 应用科学学报, 2014, 32(4): 427-433.
ZHENG Yi, ZHENG Ping. Dual thresholding method using fuzzy entropy and genetic algorithm[J]. Journal of applied sciences, 2014, 32(4): 427-433.
[18] 王树亮, 赵合计. 基于改进粒子群算法的多阈值灰度图像分割[J]. 计算机应用, 2012, 32(S2): 147-150.
WANG Shuliang, ZHAO Heji. Multilevel thresholding gray-scale image segmentation based on improved particle swarm optimization[J]. Journal of computer applications, 2012, 32(S2): 147-150.
[19] 吴一全, 张晓杰, 吴诗婳, 等. 利用高速收敛PSO或分解进行二维灰度熵图像分割[J]. 武汉大学学报:信息科学版, 2011, 36(9): 1059-1063.
WU Yiquan, ZHANG Xiaojie, WU Shihua, et al. Two-dimensional gray entropy image thresholding based on particle swarm optimization with high speed convergence or decomposition[J]. Geomatics and information science of Wuhan university, 2011, 36(9): 1059-1063.
[20] HORNG M H. A multilevel image thresholding using the honey bee mating optimization[J]. Applied mathematics and computation, 2010, 215(9): 3302-3310.
[21] 吴一全, 纪守新, 吴诗婳, 等. 基于二维直分与斜分灰度熵的图像阈值选取[J]. 天津大学学报, 2011, 44(12): 1043-1049.
WU Yiquan, JI Shouxin, WU Shihua, et al. Gray entropy image thresholding based on 2-dimensional histogram vertical and oblique segmentation[J]. Journal of Tianjin university, 2011, 44(12): 1043-1049.
[22] 吴诗婳, 吴一全, 周建江, 等. 面向医学图像分割的直线截距直方图倒数交叉熵方法[J]. 数据采集与处理, 2015, 30(5): 982-992.
WU Shihua, WU Yiquan, ZHOU Jianjiang, et al. Segmentation method based on line intercept histogram reciprocal cross entropy for medical image[J]. Journal of data acquisition and processing, 2015, 30(5): 982-992.
[23] 吴一全, 龙云淋. 基于直线截距直方图的Arimoto熵或Arimoto灰度熵的食品图像分割[J]. 现代食品科技, 2016, 32(1): 164-169.
WU Yiquan, LONG Yunlin. Food image segmentation based on line intercept histogram Arimoto entropy or Arimoto gray entropy[J]. Modern food science and technology, 2016, 32(1): 164-169.
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备注/Memo

收稿日期:2016-09-28。
基金项目:国家自然科学基金项目(61573183);城市空间信息工程北京市重点实验室开放基金项目(2014203);江西省数字国土重点实验室开放基金项目(DLLJ201412);江苏省大数据分析技术重点实验室开放基金项目(KXK1403);浙江省信号处理重点实验室开放基金项目(ZJKL_6_SP-OP2014-02);江苏高校优势学科建设工程项目(2012).
作者简介:吴诗婳,女,硕士研究生,主要研究方向为图像处理。发表学术论文多篇;吴一全,男,教授,博士生导师,博士,主要研究方向为图像处理与分析、目标检测与识别、智能信息处理。发表学术论文280余篇;周建江,男,教授,博士生导师,博士,主要研究方向雷达目标特性分析、特征控制与目标识别、机载电子信息系统、DSP技术。
通讯作者:吴一全.E-mail:nuaaimage@163.com.

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