[1]王立国,赵亮,石瑶.基于最大最小距离的高光谱遥感图像波段选择[J].智能系统学报,2018,13(1):131-137.[doi:10.11992/tis.201703023]
WANG Liguo,ZHAO Liang,SHI Yao.Maximin distance algorithm-based band selection for hyperspectral imagery[J].CAAI Transactions on Intelligent Systems,2018,13(1):131-137.[doi:10.11992/tis.201703023]
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WANG Liguo,ZHAO Liang,SHI Yao.Maximin distance algorithm-based band selection for hyperspectral imagery[J].CAAI Transactions on Intelligent Systems,2018,13(1):131-137.[doi:10.11992/tis.201703023]
基于最大最小距离的高光谱遥感图像波段选择
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
13
期数:
2018年第1期
页码:
131-137
栏目:
学术论文—机器感知与模式识别
出版日期:
2018-01-24
- Title:
- Maximin distance algorithm-based band selection for hyperspectral imagery
- Keywords:
- hyperspectral images; band selection; band clustering; unsupervised; maximin distance; K-medoids clustering; maximum likelihood method; classification
- 分类号:
- TN911.73;TP391
- 摘要:
- 为减少高光谱遥感图像光谱空间冗余，降低后续处理的计算复杂度，提出一种基于最大最小距离的高光谱图像波段选择算法。首先计算波段标准差，选定标准差最大的波段作为初始中心；然后使用最大最小距离算法得到相对距离较远的聚类中心，对波段进行聚类；最后使用K中心点算法更新聚类中心。实验仿真结果表明：通过基于最大最小距离算法选择的波段，能够选出同时满足信息量大、相关性小的要求的波段子集，并将获得的波段组合用于高光谱图像分类时，可以得到较好的分类精度。
- Abstract:
- In this paper, we propose a hyperspectral-image band-selection algorithm based on the maximin distance to reduce the spectral redundancy of hyperspectral remote sensing images as well as the associated computational complexity. First, the algorithm computes the standard deviation of all bands and selects the one with the maximum standard deviation as the initial center. Then, to cluster the bands, we use the maximin distance algorithm to obtain centers that are relatively farther away. Finally, we use the k-medoids algorithm to update the clustering center. The experimental results show that the bands selected by the maximin distance algorithm can satisfy the demands associated with a large amount of information and relatively low correlation. At the same time, when the obtained bands are combined for hyperspectral image classification, higher classification accuracy can be achieved.
- 参考文献/References:
-
[1] AGARWAL A, EL-GHAZAWI T, EL-ASKARY H, et al. Efficient hierarchical-PCA dimension reduction for hyperspectral imagery[C]//Proceedings of 2007 IEEE International Symposium on Signal Processing and Information Technology. Giza, Egypt, 2007: 353-356.
[2] WANG Jing, CHANG C I. Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis[J]. IEEE transactions on geoscience and remote sensing, 2006, 44(6): 1586-1600.
[3] LI Wei, PRASAD S, FOWLER J E, et al. Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J]. IEEE transactions on geoscience and remote sensing, 2012, 50(4): 1185-1198.
[4] 刘雪松, 葛亮, 王斌, 等. 基于最大信息量的高光谱遥感图像无监督波段选择方法[J]. 红外与毫米波学报, 2012, 31(2): 166-170, 176.
LIU Xuesong, GE Liang, WANG Bin, et al. An unsupervised band selection algorithm for hyperspectral imagery based on maximal information[J]. Journal of infrared and millimeter waves, 2012, 31(2): 166-170, 176.
[5] FENG Jie, JIAO L C, ZHANG Xiangrong, et al. Hyperspectral band selection based on trivariate mutual information and clonal selection[J]. IEEE transactions on geoscience and remote sensing, 2014, 52(7): 4092-4105.
[6] 王立国, 邓禄群, 张晶. 改进的SGA端元选择的快速方法[J]. 应用科技, 2010, 37(4), 19-22
WANG Liguo, DENG Luqun, ZHANG Jing. A fast endmember selection method based on simplex growing algorithm[J]. Applied science and technology, 2010,37(4), 1-22
[7] CHANG C I, DU Qian, SUN T L, et al. A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification[J]. IEEE transactions on geoscience and remote sensing, 1999, 37(6): 2631-2641.
[8] CHANG C I, WANG Su. Constrained band selection for hyperspectral imagery[J]. IEEE transactions on geoscience and remote sensing, 2006, 44(6): 1575-1585.
[9] 刘春红, 赵春晖, 张凌雁. 一种新的高光谱遥感图像降维方法[J]. 中国图象图形学报, 2005, 10(2): 218-222.
LIU Chunhong, ZHAO Chunhui, ZHANG Lingyan. A new method of hyperspectral remote sensing image dimensional reduction[J]. Journal of image and graphics, 2015, 10(2): 218-222.
[10] AHMAD M, HAQ I U, MUSHTAQ Q, et al. A new statistical approach for band clustering and band selection using K-means clustering[J]. IACSIT international journal of engineering and technology, 2011, 3(6): 606-614.
[11] 秦方普, 张爱武, 王书民, 等. 基于谱聚类与类间可分性因子的高光谱波段选择[J]. 光谱学与光谱分析, 2015, 35(5): 1357-1364.
QIN Fangpu, ZHANG Aiwu, WANG Shumin, et al. Hyperspectral band selection based on spectral clustering and inter-class separability factor[J]. Spectroscopy and spectral analysis, 2015, 35(5): 1357-1364.
[12] DUECK D. Affinity propagation: clustering data by passing messages[D]. Toronto, Canada: University of Toronto, 2009.
[13] 成卫青, 卢艳虹. 一种基于最大最小距离和SSE的自适应聚类算法[J]. 南京邮电大学学报: 自然科学版, 2015, 35(2): 102-107.
CHENG Weiqing, LU Yanhong. Adaptive clustering algorithm based on maximum and minmum distances, and SSE[J]. Journal of Nanjing university of posts and telecommunications: natural science edition, 2015,35(2): 102-107
[14] 刘颖, 谷延锋, 张晔, 等. 一种高光谱图像波段选择的快速混合搜索算法[J]. 光学技术, 2007, 33(2): 258-261, 265.
LIU Ying, GU Yanfeng, ZHANG Ye, et al. A fast hybrid search algorithm for band selection in hyperspectral images[J]. Optical technique, 2007, 33(2): 258-261, 265.
[15] 王立国, 肖倩, 结合Gabor滤波和同质性判定的高光谱图像分类[J]. 应用科技, 2013, 40(4): 21-26.
WANG Liguo, XIAO Qian. Hyperspectral imagery classification combined with Gabor filtering and homogeneity discrimination[J]. Applied science and technology, 2013,40(4): 21-26.
- 相似文献/References:
-
[1]李士进,常纯,余宇峰,等.基于多分类器组合的高光谱图像波段选择方法[J].智能系统学报,2014,9(3):372.[doi:10.3969/j.issn.1673-4785.201404006]
LI Shijin,CHANG Chun,YU Yufeng,et al.Multi-classifier combination-based hyperspectral band selection[J].CAAI Transactions on Intelligent Systems,2014,9():372.[doi:10.3969/j.issn.1673-4785.201404006]
备注/Memo
收稿日期:2017-03-17。
基金项目:国家自然科学基金项目（61675051）；国家教育部博士点基金项目（20132304110007）；黑龙江省自然科学基金项目（F201409）.
作者简介:王立国,男,1974年生,教授,博士生导师,主要研究方向为遥感图像处理与机器学习。主持国家自然科学基金3项,发表学术论文150余篇,其中被SCI检索40余篇,EI检索100余篇,授权国家专利20余项,出版专著2部;赵亮,女,1987年生,博士研究生,主要研究方向为高光谱遥感图像波段选择与分类,发表学术论文8篇;石瑶,女,1988年生,博士研究生,主要研究方向为高光谱遥感图像亚像元定位。
通讯作者:王立国.E-mail:wangliguo@hrbeu.edu.cn.
更新日期/Last Update:
2018-02-01