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基于尺度不变特征变换和区域互信息优化的多源遥感图像配准

赵辽英 吕步云 厉小润 陈淑涵

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基于尺度不变特征变换和区域互信息优化的多源遥感图像配准

赵辽英, 吕步云, 厉小润, 陈淑涵

Multi-source remote sensing image registration based on scale-invariant feature transform and optimization of regional mutual information

Zhao Liao-Ying, Lü Bu-Yun, Li Xiao-Run, Chen Shu-Han
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  • 为了进一步提高遥感图像配准精度, 提出了尺度不变特征变换(SIFT)结合区域互信息优化的遥感图像配准方法. 首先利用混沌序列的随机性和遍历性, 提出一种混沌量子粒子群优化(CQPSO)算法, 在量子粒子群优化(QPSO)算法迭代陷入早熟收敛时, 采用一种新的机理引入混沌序列, 进化粒子克服早熟. 图像配准算法分为预配准和精配准两个过程. 基于SIFT算法提取特征点, 经匹配和有效地外点排除完成预配准, 然后对匹配特征点坐标进行亚像素级微调, 通过最小二乘法求得一系列匹配参数构造初始粒子群, 最后利用混沌量子粒子群优化区域互信息完成精配准, 得到最优匹配参数. 用一些标准测试函数对所提出的CQPSO和QPSO及粒子群优化(PSO)算法进行了实验比较, 另外, 对SIFT, SIFT结合PSO算法优化区域互信息, SIFT结合QPSO算法优化区域互信息和SIFT结合CQPSO算法优化区域互信息(SRC)等四种算法进行了不同分辨率遥感图像配准实验比较和不同时相遥感图像配准实验比较, 实验结果验证了所提出的CQPSO算法的优越性和SRC配准方法的有效性.
    In order to further improve the precision of remote sensing image registration, we propose a new registration scheme by combining the scale-invariant feature transform (SIFT) and the optimization of regional mutual information in this paper. Firstly, taking advantage of the randomness and ergodicity of chaotic sequence, we present a new chaos quantum-behaved particle swarm optimization (CQPSO) algorithm to solve the premature convergence problem of the quantum particle swarm optimization (QPSO) algorithm. By taking full account of the quantity differences among the values of different dimensions for the particle location information, small disturbances are generated as the Hadamard product of chaotic sequence and the particle location information. Before being added to the particle location information, the small disturbances are adjusted by an evolutionary parameter to ensure that each new particle location information is within the scope of reasonable evolution. The image registration scheme consists of two processes, namely the pre-registration process and fine coregistration process. The pre-registration process is implemented by the SIFT approach with a reliable outlier removal procedure. By the repetitive fine-tuning of several selected matched feature point coordinates, a series of registration parameters is estimated by a least square method and used to construct initial particle swarms. Next, the fine coregistration process is implemented to obtain the optimal match parameters by maximizing regional mutual information based on CQPSO. The proposed CQPSO algorithm is tested on several benchmark functions and compared with QPSO as well as standard PSO experimentally. Furthermore, comparative experiments are carried out on the registration of remote sensing images with different ground resolutions and the registration of remote sensing images at different phases by using four algorithms: the SIFT algorithm, SIFT combined with PSO algorithm, SIFT combined with QPSO algorithm, and SIFT combined with CQPSO algorithm. The regional mutual information, root mean square error, and the joint histogram are used to evaluate the performance of the algorithms. The experimental results verify the superiority of CQPSO and the effectiveness of the proposed registration scheme.
    • 基金项目: 浙江省自然科学基金(批准号:LY13F020044,LZ14F030004)和国家自然科学基金(批准号:61171152)资助的课题.
    • Funds: Project supported by the Natural Science Foundation of Zhejiang Province, China (Grant Nos. LY13F020044, LZ14F030004) and the National Natural Science Foundation of China (Grant No. 61171152).
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    [2]

    Xie W D, Zhou Y H, Kou R L 2011 J. Tongji Univ. (Nat. Sci.) 39 1233 (in Chinese) [谢维达, 周宇恒, 寇若岚 2011 同济大学学报(自然科学版) 39 1233]

    [3]

    Chen Q S, Defrise M, Deconinck M 1994 IEEE Trans. Pattern Anal. Mach. Intell. 16 1156

    [4]

    Suri S, Reinartz P 2010 IEEE Trans. Geosci. Remote Sens. 48 939

    [5]

    Lowe D G 2004 Int. J. Comput Vision 60 91

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    Harris C, Stephens M 1988 Proceedings of Fourth Alvey Vision Conference Manchester, UK, August 31-September 2, 1988 p147

    [7]

    Ling Z G, Liang Y, Cheng Y M, Pan Q, Shen H 2010 Acta Electron. Sin. 38 2892 (in Chinese) [凌志刚, 梁彦, 程咏梅, 潘泉, 沈贺 2010 电子学报 38 2892]

    [8]

    Li L X 2013 M.S.Dissertation (Chengdu: University of Electronic Science and Technology of China) (in Chinese) [李龙勋 2013 硕士学位论文 (成都: 电子科技大学)]

    [9]

    Huang Q Q, Yang J, Wang C Y, Chen J B, Meng Y 2012 International Geoscience and Remote Sensing Symposium (IGARSS) Munich, Germany, July 22-27, 2012 p2360

    [10]

    Gong M G, Zhao S M, Jiao L C, Tian D Y, Wang S 2014 IEEE Trans. Geosci. Remote Sens. 52 4328

    [11]

    Liang J Y, Liu X P, Huang K N, Li X, Wang D G, Wang X W 2014 IEEE Trans. Geosci. Remote Sens. 52 603

    [12]

    Mikolajczyk K, Schmid C 2003 IEEE Trans. Geosci. Remote Sens. 41 2445

    [13]

    Wang C X, Kong Y P, Dong L L 2012 Intelligent Optimization Algorithms and Their Applications (Xi’an: Xibei University Press) p105 (in Chinese) [王超学, 孔月萍, 董丽丽 2012 智能优化算法与应用 (西安: 西北大学出版社) 第105页]

    [14]

    Sun J 2012 Acta Phys. Sin. 61 060302 (in Chinese) [李盼池, 王海英, 宋考平, 杨二龙 2012 物理学报 61 060302]

    [15]

    Li X R, Jin Y X 2014 Acta Phys. Sin. 63 089101 (in Chinese) [王跃钢, 文超斌, 左朝阳, 杨家胜, 郭志斌 2014 物理学报 63 089101]

    [16]

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  • [1]

    Chen Z G, Yin F C, Sun F 2009 Acta Opt. Sin. 29 2744 (in Chinese) [陈志刚, 尹福昌, 孙孚 2009 光学学报 29 2744]

    [2]

    Xie W D, Zhou Y H, Kou R L 2011 J. Tongji Univ. (Nat. Sci.) 39 1233 (in Chinese) [谢维达, 周宇恒, 寇若岚 2011 同济大学学报(自然科学版) 39 1233]

    [3]

    Chen Q S, Defrise M, Deconinck M 1994 IEEE Trans. Pattern Anal. Mach. Intell. 16 1156

    [4]

    Suri S, Reinartz P 2010 IEEE Trans. Geosci. Remote Sens. 48 939

    [5]

    Lowe D G 2004 Int. J. Comput Vision 60 91

    [6]

    Harris C, Stephens M 1988 Proceedings of Fourth Alvey Vision Conference Manchester, UK, August 31-September 2, 1988 p147

    [7]

    Ling Z G, Liang Y, Cheng Y M, Pan Q, Shen H 2010 Acta Electron. Sin. 38 2892 (in Chinese) [凌志刚, 梁彦, 程咏梅, 潘泉, 沈贺 2010 电子学报 38 2892]

    [8]

    Li L X 2013 M.S.Dissertation (Chengdu: University of Electronic Science and Technology of China) (in Chinese) [李龙勋 2013 硕士学位论文 (成都: 电子科技大学)]

    [9]

    Huang Q Q, Yang J, Wang C Y, Chen J B, Meng Y 2012 International Geoscience and Remote Sensing Symposium (IGARSS) Munich, Germany, July 22-27, 2012 p2360

    [10]

    Gong M G, Zhao S M, Jiao L C, Tian D Y, Wang S 2014 IEEE Trans. Geosci. Remote Sens. 52 4328

    [11]

    Liang J Y, Liu X P, Huang K N, Li X, Wang D G, Wang X W 2014 IEEE Trans. Geosci. Remote Sens. 52 603

    [12]

    Mikolajczyk K, Schmid C 2003 IEEE Trans. Geosci. Remote Sens. 41 2445

    [13]

    Wang C X, Kong Y P, Dong L L 2012 Intelligent Optimization Algorithms and Their Applications (Xi’an: Xibei University Press) p105 (in Chinese) [王超学, 孔月萍, 董丽丽 2012 智能优化算法与应用 (西安: 西北大学出版社) 第105页]

    [14]

    Sun J 2012 Acta Phys. Sin. 61 060302 (in Chinese) [李盼池, 王海英, 宋考平, 杨二龙 2012 物理学报 61 060302]

    [15]

    Li X R, Jin Y X 2014 Acta Phys. Sin. 63 089101 (in Chinese) [王跃钢, 文超斌, 左朝阳, 杨家胜, 郭志斌 2014 物理学报 63 089101]

    [16]

    Liu F C, Jia Y F, Ren L N 2013 Acta Phys. Sin. 62 120509 (in Chinese) [刘福才, 贾亚飞, 任丽娜 2013 物理学报 62 120509]

计量
  • 文章访问数:  2235
  • PDF下载量:  237
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-10-20
  • 修回日期:  2014-11-27
  • 刊出日期:  2015-06-05

基于尺度不变特征变换和区域互信息优化的多源遥感图像配准

  • 1. 杭州电子科技大学计算机应用技术研究所, 杭州 310018;
  • 2. 浙江大学电气工程学院, 杭州 310027
    基金项目: 

    浙江省自然科学基金(批准号:LY13F020044,LZ14F030004)和国家自然科学基金(批准号:61171152)资助的课题.

摘要: 为了进一步提高遥感图像配准精度, 提出了尺度不变特征变换(SIFT)结合区域互信息优化的遥感图像配准方法. 首先利用混沌序列的随机性和遍历性, 提出一种混沌量子粒子群优化(CQPSO)算法, 在量子粒子群优化(QPSO)算法迭代陷入早熟收敛时, 采用一种新的机理引入混沌序列, 进化粒子克服早熟. 图像配准算法分为预配准和精配准两个过程. 基于SIFT算法提取特征点, 经匹配和有效地外点排除完成预配准, 然后对匹配特征点坐标进行亚像素级微调, 通过最小二乘法求得一系列匹配参数构造初始粒子群, 最后利用混沌量子粒子群优化区域互信息完成精配准, 得到最优匹配参数. 用一些标准测试函数对所提出的CQPSO和QPSO及粒子群优化(PSO)算法进行了实验比较, 另外, 对SIFT, SIFT结合PSO算法优化区域互信息, SIFT结合QPSO算法优化区域互信息和SIFT结合CQPSO算法优化区域互信息(SRC)等四种算法进行了不同分辨率遥感图像配准实验比较和不同时相遥感图像配准实验比较, 实验结果验证了所提出的CQPSO算法的优越性和SRC配准方法的有效性.

English Abstract

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