使用量子优化算法进行高光谱遥感影像处理综述
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摘要
高光谱遥感技术从20世纪80年代出现以来,已迅速成为对地观测的重要组成部分,其影像信息提取是地物信息提取的主要数据来源。高光谱遥感影像除提供地物的空间信息之外,其成百上千个波段携带的光谱信息所提供的光谱诊断能力可以对地物目标进行精细化解译,大大增强了对地物信息的提取能力。充分利用高光谱遥感影像丰富的光谱信息对地物目标进行精细化解译成为近年来遥感领域的研究热点。对基于量子优化算法的高光谱遥感影像处理方法进行阐述,介绍了量子优化算法的发展与技术,并概括了其在高光谱遥感影像中的应用,并对量子优化算法在高光谱遥感影像处理中的应用发展提出建议和展望。
Hyperspectral remote sensing technology has become an important part of ground observation since the 1980 s, and it is the main data source of information acquisition for ground objects. Hyperspectral image(HSIs) not only contains spatial information, but also contains abundant spectral information with tens to hundreds of contiguous spectral bands. The abundant spectral information of HSIs can help us better identify ground objects, which has greatly improved our ability to qualitatively and quantitatively sense the earth's surface. It has been intensively researched to make full use of both spatial and spectral information of HSIs, so as to accurately obtain the information of ground objects. This paper reviews quantum optimization algorithm-based hyperspectral image processing me-thods. The development and methodology of quantum optimization algorithm as well as its application in hyperspectral image processing are introduced. And some suggestion and expectation for further study of the quantum optimization algorithm-based hyperspectral image processing are given.
Hyperspectral remote sensing technology has become an important part of ground observation since the 1980 s, and it is the main data source of information acquisition for ground objects. Hyperspectral image(HSIs) not only contains spatial information, but also contains abundant spectral information with tens to hundreds of contiguous spectral bands. The abundant spectral information of HSIs can help us better identify ground objects, which has greatly improved our ability to qualitatively and quantitatively sense the earth's surface. It has been intensively researched to make full use of both spatial and spectral information of HSIs, so as to accurately obtain the information of ground objects. This paper reviews quantum optimization algorithm-based hyperspectral image processing me-thods. The development and methodology of quantum optimization algorithm as well as its application in hyperspectral image processing are introduced. And some suggestion and expectation for further study of the quantum optimization algorithm-based hyperspectral image processing are given.
引文
[1] Zhang Liangpei, Du Bo, Zhang Lefei. Hyperspectral Image Processing[M]. Beijing: Science Press, 2014(张良培,杜博,张乐飞.高光谱遥感影像处理[M]. 北京:科学出版社, 2014)
[2] An Ru, Lu Caihong, Wang Huilin, et al. Remote Sensing Identification of Rangeland Degradation Using Hyperion Hyperspectral Image in a Typical Area for Three-River Headwater Region, Qinghai, China[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 399-405(安如, 陆彩红, 王慧麟,等. 三江源典型区草地退化Hyperion高光谱遥感识别研究[J]. 武汉大学学报·信息科学版, 2018, 43(3):399-405)
[3] Onojeghuo A O, Blackburn G A, Huang J, et al. Applications of Satellite’Hyper-Sensing’ in Chinese Agriculture: Challenges and Opportunities[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 64:62-86
[4] Qin Zhanfei, Shen Jian, Xie Baoni, et al. Hyperspectral Estimation Model for Predicting LAI of Rice in Ningxia Irrigation Zone[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8):1 159-1 166 (秦占飞, 申健, 谢宝妮,等. 引黄灌区水稻叶面积指数的高光谱估测模型[J]. 武汉大学学报·信息科学版, 2017, 42(8):1 159-1 166)
[5] Yue J, Feng H, Yang G, et al. A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy[J]. Remote Sensing, 2018, 10(1):66
[6] Zhong Y, Zhang L. An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3):894-909
[7] Zhong Y, Zhang S, Zhang L. Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5):2 290-2 301
[8] Zhong Y, Zhao L, Zhang L. An Adaptive Differential Evolution Endmember Extraction Algorithm for Hyperspectral Remote Sensing Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6):1 061-1 065
[9] Zhong Y, Cao Q, Zhao J, et al. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data[J]. Remote Sen-sing, 2017, 9(8):868
[10] Feng J, Jiao L C, Zhang X, et al. Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7):4 092-4 105
[11] Zhang B, Sun X, Gao L, et al. Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Discrete Particle Swarm Optimization Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4 173-4 176
[12] Zhang B, Sun X, Gao L, et al. Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(7):2 635-2 646
[13] Zhang B, Gao J, Gao L, et al. Improvements in the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):522-530
[14] Shor P W. Algorithms for Quantum Computation:[C]. IEEE Symposium on Foundations of Computer Science, Mitwaukee, WI, USA, 1995
[15] Grover L K. A Fast Quantum Mechanical Algorithm for Database Search[C]. 28th ACM Symposium on Theory of Computing, Philadelphia,USA, 1996
[16] Li P, Li S. Quantum Ant Colony Algorithm for Continuous Space Optimization[J]. Control Theory and Applications, 2008, 25(2):237-241
[17] Han K H, Kim J H. Genetic Quantum Algorithm and Its Application to Combinatorial Optimization Problem[C]. The IEEE 2000 Congress on Evolutionary Computation, La Jolla, USA, 2002
[18] Sun J, Feng B, Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior[C]. IEEE Congress on Evolutionary Computation, California, USA, 2004
[19] Hinterding R. Representation, Constraint Satisfaction and the Knapsack Problem[C]. The IEEE Congress on Evolutionary Computation, Washington D C, USA, 1999
[20] Hey T. Quantum Computing: An Introduction[J]. Computing and Control Engineering Journal, 1998, 10(3):105-112
[21] Clerc M, Kennedy J. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(1):58-73
[22] Sun J, Fang W, Wu X, et al. Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection[J]. Evolutionary Computation, 2012, 20(3):349-393
[23] Du B, Zhang L. Target Detection Based on a Dynamic Subspace[J]. Pattern Recognition, 2014, 47(1):344-358
[24] Li N, Du P, Zhao H. Independent Component Analysis Based on Improved Quantum Genetic Algorithm: Application in Hyperspectral Images[C]. IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 2005
[25] Boardman J W. Automating Spectral Unmixing of AVIRIS Data Using Convex Geometry Concepts[C]. 4th Annu JPL Airborne Geoscience Workshop, Washington D C, USA, 1993
[26] Winter M E. N-FINDR: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data[J]. Proceedings of SPIE—The International Society for Optical Enginee-ring, 1999, 3 753:266-275
[27] Chang C I, Wu C C, Liu W, et al. A New Growing Method for Simplex-Based Endmember Extraction Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10):2 804-2 819
[28] Chan T H, Ma W K, Ambikapathi A M, et al. A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4 177-4 193
[29] Du Q. A New Sequential Algorithm for Hyperspectral Endmember Extraction[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4):695-699
[30] Luo W, Zhang B, Jia X. New Improvements in Pa-rallel Implementation of N-FINDR Algorithm[J]. IEEE Transactions on Geoscience and Remote Sen-sing, 2012, 50(10):3 648-3 659
[31] Liu R, Zhang L, Du B. A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4):1 610-1 631
[32] Xu M, Zhang L, Du B, et al. A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction[J]. Remote Sensing, 2017, 9(3):197
[33] Atkinson P M. Mapping Sub-pixel Boundaries from Remotely Sensed Images[J]. Innovations in GIS, 1997,4:167-180
[34] Villa A, Chanussot J, Benediktsson J A, et al. Unsupervised Methods for the Classification of Hyperspectral Images with Low Spatial Resolution[J]. Pattern Recognition, 2013, 46(6):1 556-1 568
[35] Thornton M W, Atkinson P M, Holland D A. Sub-pixel Mapping of Rural Land Cover Objects from Fine Spatial Resolution Satellite Sensor Imagery Using Super-Resolution Pixel-Swapping[J]. International Journal of Remote Sensing, 2006, 27(3):473-491
[36] Ertürk A, Güllü M K, Çeşmeci D, et al. Spatial Resolution Enhancement of Hyperspectral Images Using Unmixing and Binary Particle Swarm Optimization[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12):2 100-2 104
[2] An Ru, Lu Caihong, Wang Huilin, et al. Remote Sensing Identification of Rangeland Degradation Using Hyperion Hyperspectral Image in a Typical Area for Three-River Headwater Region, Qinghai, China[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 399-405(安如, 陆彩红, 王慧麟,等. 三江源典型区草地退化Hyperion高光谱遥感识别研究[J]. 武汉大学学报·信息科学版, 2018, 43(3):399-405)
[3] Onojeghuo A O, Blackburn G A, Huang J, et al. Applications of Satellite’Hyper-Sensing’ in Chinese Agriculture: Challenges and Opportunities[J]. International Journal of Applied Earth Observation and Geoinformation, 2018, 64:62-86
[4] Qin Zhanfei, Shen Jian, Xie Baoni, et al. Hyperspectral Estimation Model for Predicting LAI of Rice in Ningxia Irrigation Zone[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8):1 159-1 166 (秦占飞, 申健, 谢宝妮,等. 引黄灌区水稻叶面积指数的高光谱估测模型[J]. 武汉大学学报·信息科学版, 2017, 42(8):1 159-1 166)
[5] Yue J, Feng H, Yang G, et al. A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy[J]. Remote Sensing, 2018, 10(1):66
[6] Zhong Y, Zhang L. An Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3):894-909
[7] Zhong Y, Zhang S, Zhang L. Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(5):2 290-2 301
[8] Zhong Y, Zhao L, Zhang L. An Adaptive Differential Evolution Endmember Extraction Algorithm for Hyperspectral Remote Sensing Imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6):1 061-1 065
[9] Zhong Y, Cao Q, Zhao J, et al. Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data[J]. Remote Sen-sing, 2017, 9(8):868
[10] Feng J, Jiao L C, Zhang X, et al. Hyperspectral Band Selection Based on Trivariate Mutual Information and Clonal Selection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7):4 092-4 105
[11] Zhang B, Sun X, Gao L, et al. Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Discrete Particle Swarm Optimization Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4 173-4 176
[12] Zhang B, Sun X, Gao L, et al. Endmember Extraction of Hyperspectral Remote Sensing Images Based on the Ant Colony Optimization (ACO) Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(7):2 635-2 646
[13] Zhang B, Gao J, Gao L, et al. Improvements in the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(2):522-530
[14] Shor P W. Algorithms for Quantum Computation:[C]. IEEE Symposium on Foundations of Computer Science, Mitwaukee, WI, USA, 1995
[15] Grover L K. A Fast Quantum Mechanical Algorithm for Database Search[C]. 28th ACM Symposium on Theory of Computing, Philadelphia,USA, 1996
[16] Li P, Li S. Quantum Ant Colony Algorithm for Continuous Space Optimization[J]. Control Theory and Applications, 2008, 25(2):237-241
[17] Han K H, Kim J H. Genetic Quantum Algorithm and Its Application to Combinatorial Optimization Problem[C]. The IEEE 2000 Congress on Evolutionary Computation, La Jolla, USA, 2002
[18] Sun J, Feng B, Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior[C]. IEEE Congress on Evolutionary Computation, California, USA, 2004
[19] Hinterding R. Representation, Constraint Satisfaction and the Knapsack Problem[C]. The IEEE Congress on Evolutionary Computation, Washington D C, USA, 1999
[20] Hey T. Quantum Computing: An Introduction[J]. Computing and Control Engineering Journal, 1998, 10(3):105-112
[21] Clerc M, Kennedy J. The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(1):58-73
[22] Sun J, Fang W, Wu X, et al. Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection[J]. Evolutionary Computation, 2012, 20(3):349-393
[23] Du B, Zhang L. Target Detection Based on a Dynamic Subspace[J]. Pattern Recognition, 2014, 47(1):344-358
[24] Li N, Du P, Zhao H. Independent Component Analysis Based on Improved Quantum Genetic Algorithm: Application in Hyperspectral Images[C]. IEEE International Geoscience and Remote Sensing Symposium, Seoul, Korea, 2005
[25] Boardman J W. Automating Spectral Unmixing of AVIRIS Data Using Convex Geometry Concepts[C]. 4th Annu JPL Airborne Geoscience Workshop, Washington D C, USA, 1993
[26] Winter M E. N-FINDR: An Algorithm for Fast Autonomous Spectral End-Member Determination in Hyperspectral Data[J]. Proceedings of SPIE—The International Society for Optical Enginee-ring, 1999, 3 753:266-275
[27] Chang C I, Wu C C, Liu W, et al. A New Growing Method for Simplex-Based Endmember Extraction Algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10):2 804-2 819
[28] Chan T H, Ma W K, Ambikapathi A M, et al. A Simplex Volume Maximization Framework for Hyperspectral Endmember Extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11):4 177-4 193
[29] Du Q. A New Sequential Algorithm for Hyperspectral Endmember Extraction[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(4):695-699
[30] Luo W, Zhang B, Jia X. New Improvements in Pa-rallel Implementation of N-FINDR Algorithm[J]. IEEE Transactions on Geoscience and Remote Sen-sing, 2012, 50(10):3 648-3 659
[31] Liu R, Zhang L, Du B. A Novel Endmember Extraction Method for Hyperspectral Imagery Based on Quantum-Behaved Particle Swarm Optimization[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(4):1 610-1 631
[32] Xu M, Zhang L, Du B, et al. A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction[J]. Remote Sensing, 2017, 9(3):197
[33] Atkinson P M. Mapping Sub-pixel Boundaries from Remotely Sensed Images[J]. Innovations in GIS, 1997,4:167-180
[34] Villa A, Chanussot J, Benediktsson J A, et al. Unsupervised Methods for the Classification of Hyperspectral Images with Low Spatial Resolution[J]. Pattern Recognition, 2013, 46(6):1 556-1 568
[35] Thornton M W, Atkinson P M, Holland D A. Sub-pixel Mapping of Rural Land Cover Objects from Fine Spatial Resolution Satellite Sensor Imagery Using Super-Resolution Pixel-Swapping[J]. International Journal of Remote Sensing, 2006, 27(3):473-491
[36] Ertürk A, Güllü M K, Çeşmeci D, et al. Spatial Resolution Enhancement of Hyperspectral Images Using Unmixing and Binary Particle Swarm Optimization[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(12):2 100-2 104