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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.
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Keywords:
- remote sensing image registration /
- regional mutual information /
- chaos quantum-behaved particle swarm optimization /
- scale-invariant feature transform
[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 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 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 62 120509]
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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 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 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 62 120509]
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