[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(02):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(02):227-235.[doi:10.11992/tis.201609012]
点击复制

直线截距直方图城区遥感图像多阈值分割
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第13卷
期数:
2018年02期
页码:
227-235
栏目:
出版日期:
2018-04-15

文章信息/Info

Title:
Multi-level thresholding for remote sensing image of urban area based on line intercept histogram
作者:
吴诗婳1 吴一全12345 周建江1
1. 南京航空航天大学 电子信息工程学院, 江苏 南京 211106;
2. 城市空间信息工程北京市重点实验室, 北京 100038;
3. 江西省数字国土重点实验室, 江西 南昌 330013;
4. 江苏省大数据分析技术重点实验室, 江苏 南京 210044;
5. 浙江省信号处理重点实验室, 浙江 杭州 310023
Author(s):
WU Shihua1 WU Yiquan12345 ZHOU Jianjiang1
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China;
3. Jiangxi Province Key Labor
关键词:
城区提取遥感图像图像分割阈值化多阈值选取直线截距直方图倒数灰度熵人工蜂群优化
Keywords:
extraction of urban arearemote sensing imageimage segmentationthresholdingmulti-level threshold selectionstraight-line intercept histogramreciprocal grayscale entropyoptimization of artificial bee colony
分类号:
TP751.1;P237
DOI:
10.11992/tis.201609012
摘要:
阈值分割简单有效,但现有的单阈值方法对城区图像分割效果不佳,难以取得令人满意的结果。为了快速准确地对城区遥感图像进行分割,本文提出了基于直线截距直方图倒数灰度熵和人工蜂群优化(artificial bee colony optimization, ABC)的多阈值分割方法。首先,给出直线截距直方图的定义并建立城区遥感图像的直线截距直方图;然后,计算该直方图倒数灰度熵的大小,推导出其单阈值选取公式;最后,将其推广到多阈值选取,并利用人工蜂群优化算法,对多个阈值进行快速精确地寻优,以此最终实现城区遥感图像的多阈值分割。实验结果表明,该方法所分割的图像中多目标的形状、边缘更为准确,纹理及细节特征更加清晰,且所需运行时间仅为同类多阈值分割方法的25%,是一种行之有效的城区遥感图像分割方法。
Abstract:
Threshold segmentation is a kind of simple and effective method, however, the existing single-threshold method is hard to realize satisfactory effect in segmenting the images of urban area. In order to segment the remote sensing images of urban area quickly and accurately, a multi-threshold segmentation method based on straight-line intercept histogram, reciprocal grayscale entropy and Artificial Bee Colony (ABC) Optimization was proposed in the paper. Firstly, the straight-line intercept histogram was defined and the straight-line intercept histogram of the urban remote sensing image was established; then the value of the reciprocal grayscale entropy of the histogram was calculated and the single-threshold selection formula was deduced; finally, the application was popularized to multi-threshold selection, ABC Optimization algorithm was utilized for precise optimization of many thresholds, so as to finally realize the multi-threshold segmentation of urban remote sensing images. A large number of experiments show that, the multi-object shape and edge in the images segmented by the method are more accurate, the textures and details are more explicit, in addition, its running time is only 25% of other similar multi-threshold segmentation methods. This is a kind of effective method for segmenting the remote sensing images of urban area.

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] SIRMAÇEK 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] MAŁYSZKO 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.

相似文献/References:

[1]刘富,于鹏,刘坤.采用独立分量分析Zernike矩的遥感图像飞机目标识别[J].智能系统学报,2011,6(01):51.
 LIU Fu,YU Peng,LIU Kun.Research concerning aircraft recognition of remote sensing images based on ICA Zernike invariant moments[J].CAAI Transactions on Intelligent Systems,2011,6(02):51.
[2]龙海侠,吴淑雷,吕雁.基于多样性变异的QPSO算法的遥感图像分类[J].智能系统学报,2015,10(6):938.[doi:10.11992/tis.201507045]
 LONG Haixia,WU Shulei,LYU Yan.Classification of multispectral remote sensing image based on QPSO and diversity-mutation[J].CAAI Transactions on Intelligent Systems,2015,10(02):938.[doi:10.11992/tis.201507045]
[3]李亚飞,董红斌.基于卷积神经网络的遥感图像分类研究[J].智能系统学报,2018,13(04):550.[doi:10.11992/tis.201706078]
 LI Yafei,DONG Hongbin.Classification of remote-sensing image based on convolutional neural network[J].CAAI Transactions on Intelligent Systems,2018,13(02):550.[doi:10.11992/tis.201706078]

备注/Memo

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.
更新日期/Last Update: 1900-01-01