基于大规模多目标优化的高光谱稀疏解混算法
|
摘要
针对现有多目标稀疏解混算法中存在因随机分组策略的不足和拐点选择具有单一性,进而导致高光谱数据解混精度不高的问题,本文提出一种基于大规模多目标优化的高光谱稀疏解混算法。引入大规模多目标优化算法的决策变量分组策略,并提出有约束拐点区域选择策略求取丰度最优解,进而提高解混精度。对模拟和真实的高光谱数据进行实验,结果表明:本文算法在解混精度上有大幅度提升,与其他算法比较,可以看出本文算法得到的丰度图边缘细节处理得更好,抗噪性能更强,验证了本文提出算法的有效性和先进性。
The existing multi-objective sparse unmixing algorithm has the defect of random grouping strategy and simplicity of the knee point selection,which leads to low accuracy of the hyperspectral data unmixing. Considering this problem,this paper proposes a hyperspectral sparse unmixing algorithm based on the large-scale many-objective evolutionary optimization( LMEA) algorithm. Based on the decision variable grouping strategy of the LMEA,a constrained knee point area selection strategy is used to obtain the abundance optimal solution to improve the unmixing accuracy. Experiments on simulated and real hyperspectral data show that the proposed algorithm greatly improved the unmixing accuracy. Compared with other algorithms,the abundance map edge details obtained by this algorithm were better processed and the anti-noise performance was stronger; this verifies the effectiveness and advancement of the proposed algorithm.
The existing multi-objective sparse unmixing algorithm has the defect of random grouping strategy and simplicity of the knee point selection,which leads to low accuracy of the hyperspectral data unmixing. Considering this problem,this paper proposes a hyperspectral sparse unmixing algorithm based on the large-scale many-objective evolutionary optimization( LMEA) algorithm. Based on the decision variable grouping strategy of the LMEA,a constrained knee point area selection strategy is used to obtain the abundance optimal solution to improve the unmixing accuracy. Experiments on simulated and real hyperspectral data show that the proposed algorithm greatly improved the unmixing accuracy. Compared with other algorithms,the abundance map edge details obtained by this algorithm were better processed and the anti-noise performance was stronger; this verifies the effectiveness and advancement of the proposed algorithm.
引文
[1]张兵,孙旭.高光谱图像混合像元分解[M].北京:科学出版社,2015:3-4.
[2]LI Chang,MA Yong,GAN Yuan,et al.Sparse unmixing of hyperspectral data based on robust linear mixing model[C]//Proceedings of 2016 Visual Communications and Image Processing.Chengdu:IEEE,2017,DOI:10.1109/VCIP.2016.7805498.
[3]杨斌,王斌.高光谱遥感图像非线性解混研究综述[J].红外与毫米波学报,2017,36(2):173-185,DOI:10.11972/j.issn.1001-9014.2017.02.009.YANG Bin,WANG Bin.Review of nonlinear unmixing for hyperspectral remote sensing imagery[J].Journal of infrared and millimeter Waves,2017,36(2):173-185,DOI:10.11972/j.issn.1001-9014.2017.02.009.
[4]BIOUCAS-DIAS J M,FIGUEIREDO M A T.Alternating direction algorithms for constrained sparse regression:Application to hyperspectral unmixing[C]//Proceedings of the2nd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing.Reykjavik,Iceland:IEEE,2010:1-4,DOI:10.1109/WHISPERS.2010.5594963.
[5]GONG Maoguo,LI Hao,Luo Enhu,et al.A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing[J].IEEE transactions on evolutionary computation,2017,21(2):234-248,DOI:10.1109/TEVC.2016.2598858.
[6]ZHANG Xingyi,TIAN Ye,CHENG Ran,et al.A decision variable clustering-based evolutionary algorithm for largescale many-objective optimization[J].IEEE transactions on evolutionary computation,2018,22(1):97-112,DOI:10.1109/TEVC.2016.2600642.
[7]SHI Zhenwei,SHI Tianyang,ZHOU Min,et al.Collaborative sparse hyperspectral unmixing using l0norm[J].IEEEtransactions on geoscience and remote sensing,2018,56(9):5495-5508,DOI:10.1109/TGRS.2018.2818703.
[8]CANDE E J,TAO T.Decoding by linear programming[J].IEEE transactions on information theory,2005,51(12):4203-4215,DOI:10.1109/TIT.2005.858979.
[9]BI Xiaojun,WANG Chao.A niche-elimination operation based NSGA-III algorithm for many-objective optimization[J].Applied intelligence,2018,48(1):118-141,DOI:10.1007/s10489-017-0958-4.
[10]IORDACHE M D,BIOUCAS-DIAS J M,PLAZA A.Sparse unmixing of hyperspectral data[J].IEEE transactions on geoscience and remote sensing,2011,49(6):2014-2039,DOI:10.1109/TGRS.2010.2098413.
[11]徐夏,张宁,史振威,等.高光谱图像Pareto优化稀疏解混[J].红外与激光工程,2018,47(2):256-260,DOI:10.3788/IRLA201847.0226002.XU Xia,ZHANG Ning,SHI Zhenwei,et al.Sparse unmixing of hyperspectral images based on Pareto optimization[J].Infrared and laser engineering,2018,47(2):256-260,DOI:10.3788/IRLA201847.0226002.
[12]IORDACHE M D,BIOUCAS-DIAS J M,PLAZA A.Total variation spatial regularization for sparse hyperspectral unmixing[J].IEEE transactions on geoscience and remote sensing,2012,50(11):4484-4502,DOI:10.1109/TGRS.2012.2191590.
[2]LI Chang,MA Yong,GAN Yuan,et al.Sparse unmixing of hyperspectral data based on robust linear mixing model[C]//Proceedings of 2016 Visual Communications and Image Processing.Chengdu:IEEE,2017,DOI:10.1109/VCIP.2016.7805498.
[3]杨斌,王斌.高光谱遥感图像非线性解混研究综述[J].红外与毫米波学报,2017,36(2):173-185,DOI:10.11972/j.issn.1001-9014.2017.02.009.YANG Bin,WANG Bin.Review of nonlinear unmixing for hyperspectral remote sensing imagery[J].Journal of infrared and millimeter Waves,2017,36(2):173-185,DOI:10.11972/j.issn.1001-9014.2017.02.009.
[4]BIOUCAS-DIAS J M,FIGUEIREDO M A T.Alternating direction algorithms for constrained sparse regression:Application to hyperspectral unmixing[C]//Proceedings of the2nd Workshop on Hyperspectral Image and Signal Processing:Evolution in Remote Sensing.Reykjavik,Iceland:IEEE,2010:1-4,DOI:10.1109/WHISPERS.2010.5594963.
[5]GONG Maoguo,LI Hao,Luo Enhu,et al.A multiobjective cooperative coevolutionary algorithm for hyperspectral sparse unmixing[J].IEEE transactions on evolutionary computation,2017,21(2):234-248,DOI:10.1109/TEVC.2016.2598858.
[6]ZHANG Xingyi,TIAN Ye,CHENG Ran,et al.A decision variable clustering-based evolutionary algorithm for largescale many-objective optimization[J].IEEE transactions on evolutionary computation,2018,22(1):97-112,DOI:10.1109/TEVC.2016.2600642.
[7]SHI Zhenwei,SHI Tianyang,ZHOU Min,et al.Collaborative sparse hyperspectral unmixing using l0norm[J].IEEEtransactions on geoscience and remote sensing,2018,56(9):5495-5508,DOI:10.1109/TGRS.2018.2818703.
[8]CANDE E J,TAO T.Decoding by linear programming[J].IEEE transactions on information theory,2005,51(12):4203-4215,DOI:10.1109/TIT.2005.858979.
[9]BI Xiaojun,WANG Chao.A niche-elimination operation based NSGA-III algorithm for many-objective optimization[J].Applied intelligence,2018,48(1):118-141,DOI:10.1007/s10489-017-0958-4.
[10]IORDACHE M D,BIOUCAS-DIAS J M,PLAZA A.Sparse unmixing of hyperspectral data[J].IEEE transactions on geoscience and remote sensing,2011,49(6):2014-2039,DOI:10.1109/TGRS.2010.2098413.
[11]徐夏,张宁,史振威,等.高光谱图像Pareto优化稀疏解混[J].红外与激光工程,2018,47(2):256-260,DOI:10.3788/IRLA201847.0226002.XU Xia,ZHANG Ning,SHI Zhenwei,et al.Sparse unmixing of hyperspectral images based on Pareto optimization[J].Infrared and laser engineering,2018,47(2):256-260,DOI:10.3788/IRLA201847.0226002.
[12]IORDACHE M D,BIOUCAS-DIAS J M,PLAZA A.Total variation spatial regularization for sparse hyperspectral unmixing[J].IEEE transactions on geoscience and remote sensing,2012,50(11):4484-4502,DOI:10.1109/TGRS.2012.2191590.