基于混沌布谷鸟优化的二维Tsallis交叉熵建筑物遥感图像分割
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摘要
为了进一步提升建筑物遥感图像分割的准确性和运算速度,本文提出了基于混沌布谷鸟优化的二维Tsallis交叉熵的建筑物遥感图像分割方法。首先给出了二维Tsallis交叉熵的阈值选取公式,然后将Logistic混沌映射引入布谷鸟算法,进一步加快布谷鸟算法的收敛速度,最后通过该混沌布谷鸟算法优化基于二维Tsallis交叉熵的阈值寻找过程,并以得到的最优阈值分割建筑物遥感图像。大量实验结果表明,与二维倒数交叉熵法、二维Tsallis熵法、基于混沌粒子群优化的二维Tsallis灰度熵法等方法相比较,本文方法分割的目标更为准确,细节更为清晰,且运算时间更短。
In order to improve the accuracy and running speed of segmentation of building remote sensing images,a threshold segmentation method based on the 2-D Tsallis cross entropy image threshold selection using chaotic cuckoo search optimization is proposed. Firstly,the formula of 2-D Tsallis cross entropy threshold selection based on the histogram is derived. Next,in order to improve the convergence rate,logistic chaotic map is applied to the cuckoo search algorithm. Finally,the proposed chaotic cuckoo search algorithm is utilized for precise optimization of thresholds based on the 2-D Tsallis cross entropy,so as to realize the threshold segmentation of building remote sensing images with optimal threshold. A large number of experiments show that,compared with 2-D reciprocal cross entropy thresholding method,2-D Tsallis entropy thresholding method,2-D Tsallis gray entropy thresholding method based on chaotic particle swarm optimization and so on,the objects in the images segmented by the proposed method are more accurate,the details are more explicit,in addition,its running time is shorter.
In order to improve the accuracy and running speed of segmentation of building remote sensing images,a threshold segmentation method based on the 2-D Tsallis cross entropy image threshold selection using chaotic cuckoo search optimization is proposed. Firstly,the formula of 2-D Tsallis cross entropy threshold selection based on the histogram is derived. Next,in order to improve the convergence rate,logistic chaotic map is applied to the cuckoo search algorithm. Finally,the proposed chaotic cuckoo search algorithm is utilized for precise optimization of thresholds based on the 2-D Tsallis cross entropy,so as to realize the threshold segmentation of building remote sensing images with optimal threshold. A large number of experiments show that,compared with 2-D reciprocal cross entropy thresholding method,2-D Tsallis entropy thresholding method,2-D Tsallis gray entropy thresholding method based on chaotic particle swarm optimization and so on,the objects in the images segmented by the proposed method are more accurate,the details are more explicit,in addition,its running time is shorter.
引文
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[19]陈亮,卢厚清.求解连续函数优化的自适应布谷鸟搜索算法[J].解放军理工大学学报(自然科学版),2015,16(3):299-304.Chen Liang,Lu Houqing.Self-adaptive cuckoo search algorithm for continuous function optimization problems[J].Journal of PLA University of Science&Technology(Natural Science Edition),2015,16(3):299-304.
[20]刘志刚,许少华,李盼池,等.基于量子衍生布谷鸟的脊波过程神经网络及TOC预测[J].控制与决策,2017,32(6):1115-1120.Liu Zhigang,Xu Shaohua,Li Panchi,et al.Ridgelet process neural networks based on quantum-inspired cuckoo search and application for TOC prediction[J].Control and Decision,2017,32(6):1115-1120.
[21]Yang X S.Nature-inspired metaheuristic algorithms[M].2nd ed.UK:Luniver Press,2010:11-16.
[2]Zhu Q,Jiang W,Zhang J.Feature line based building detection and reconstruction from oblique airborne imagery[J].ISPRSInternational Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2015,5(4):199-204.
[3]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.
[4]安文,杨俊峰,赵羲,等.高分辨率遥感影像的建筑物自动提取[J].测绘科学,2014,39(11):80-84.An Wen,Yang Junfeng,Zhao Xi,et al.Buildings automatic extraction from high resolution RS images[J].Science of Surveying&Mapping,2014,39(11):80-84.
[5]Yang J,He Y,Caspersen J.A self-adapted threshold-based region merging method for remote sensing image segmentation[C]//Geoscience and Remote Sensing Symposium.Beijing:IEEE,2016:6320-6323.
[6]章勇,杨英宝,孔令婷,等.基于阈值分割的LIDAR建筑物提取[J].测绘与空间地理信息,2015,38(2):27-29.Zhang Yong,Yang Yingbao,Kong Lingting,et al.LIDAR building extraction based on threshold segmentation[J].Geomatics&Spatial Information Technology,2015,38(2):27-29.
[7]吴一全,孟天亮,吴诗婳.图像阈值分割方法研究进展20年(1994—2014)[J].数据采集与处理,2015,30(1):1-23.Wu Yiquan,Meng Tianliang,Wu Shihua.Research progress of image thresholding methods in recent 20 years(1994-2014)[J].Journal of Data Acquisition&Processing,2015,30(1):1-23.
[8]Kapur J N,Sahoo P K,Wong A K C.A new method for grey-level picture thresholding using the entropy of the histogram[J].Computer Vision,Graphics and Image Processing,1985,29(1):273-285.
[9]Brink A D.Thresholding of digital images using two-dimensional entropies[J].Pattern Recognition,1992,25(8):803-808.
[10]Sahoo P K,Arora G.Image thresholding using two-dimensional Tsallis-Havrda-Charvát entropy[J].Pattern Recognition Letters,2006,27(6):520-528.
[11]吴一全,吴诗婳,张晓杰.利用混沌PSO或分解的2维Tsallis灰度熵阈值分割[J].中国图象图形学报,2012,17(8):902-910.Wu Yiquan,Wu Shihua,Zhang Xiaojie.Two-dimensional Tsallis gray entropy image thresholding using chaotic particle swarm optimization or decomposition[J].Journal of Image&Graphics,2012,17(8):902-910.
[12]Li C K,Lee C H.Minimum cross entropy thresholding[J].Pattern Recognition,1993,26(4):617-625.
[13]吴一全,孟天亮.分解的二维倒数交叉熵图像阈值选取[J].信号处理,2013,29(7):800-808.Wu Yiquan,Meng Tianliang.Image threshold selection using two-dimensional reciprocal cross entropy based on decomposition[J].Journal of Signal Processing,2013,29(7):800-808.
[14]唐英干,邸秋艳,关新平,等.基于最小Tsallis交叉熵的阈值图像分割方法[J].仪器仪表学报,2008,29(9):1868-1872.Tang Yinggan,Di Qiuyan,Guan Xinping,et al.Method for thresholding image segmentation based on minimum Tsallis-cross entropy[J].Chinese Journal of Scientific Instrument,2008,29(9):1868-1872.
[15]Lan J,Zeng Y.Multi-threshold image segmentation using maximum fuzzy entropy based on a new 2D histogram[J].OptikInternational Journal for Light and Electron Optics,2013,124(18):3756-3760.
[16]唐英干,邸秋艳,赵立兴,等.基于二维最小Tsallis交叉熵的图像阈值分割方法[J].物理学报,2009,58(1):9-15.Tang Yinggan,Di Qiuyan,Zhao Lixing,et al.Image thresholding segmentation based on two-dimensional minimum Tsalliscross entropy[J].Acta Physica Sinica,2009,58(1):9-15.
[17]Cuevas E,Zaldivar D,Sossa H.A multi-threshold segmentation approach based on artificial bee colony optimization[J].Applied Intelligence,2012,37(3):321-336.
[18]Yang X S,Deb S.Cuckoo search via levy flights[C]//Proceedings of World Congress on Nature and Biologically Inspired Computing.Coimbatore:IEEE,2009:210-214.
[19]陈亮,卢厚清.求解连续函数优化的自适应布谷鸟搜索算法[J].解放军理工大学学报(自然科学版),2015,16(3):299-304.Chen Liang,Lu Houqing.Self-adaptive cuckoo search algorithm for continuous function optimization problems[J].Journal of PLA University of Science&Technology(Natural Science Edition),2015,16(3):299-304.
[20]刘志刚,许少华,李盼池,等.基于量子衍生布谷鸟的脊波过程神经网络及TOC预测[J].控制与决策,2017,32(6):1115-1120.Liu Zhigang,Xu Shaohua,Li Panchi,et al.Ridgelet process neural networks based on quantum-inspired cuckoo search and application for TOC prediction[J].Control and Decision,2017,32(6):1115-1120.
[21]Yang X S.Nature-inspired metaheuristic algorithms[M].2nd ed.UK:Luniver Press,2010:11-16.