[1]崔颖,王雪婷,刘述彬,等.基于改进SPA算法的高效降维方法[J].应用科技,2018,45(05):51-55.[doi:10.11991/yykj.201712002]
 CUI Ying,WANG Xueting,LIU Shubin,et al.An efficient reduction method based on the improved successive projection algorithm[J].yykj,2018,45(05):51-55.[doi:10.11991/yykj.201712002]
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第45卷
期数:
2018年05期
页码:
51-55
栏目:
现代电子技术
出版日期:
2018-09-15

文章信息/Info

Title:
An efficient reduction method based on the improved successive projection algorithm
作者:
崔颖12 王雪婷1 刘述彬2 陆忠军2
1. 哈尔滨工程大学 信息与通信工程学院, 黑龙江 哈尔滨 150001;
2. 黑龙江省农业科学院 遥感技术中心, 黑龙江 哈尔滨 150086
Author(s):
CUI Ying12 WANG Xueting1 LIU Shubin2 LU Zhongjun2
1. College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China;
2. Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
关键词:
高光谱图像波段选择降维连续投影算法峰度偏度支持向量机相关向量机
Keywords:
hyperspectral imageband selectiondimensionality reductionsuccessive projection algorithmkurtosisskewnesssupport vector machinerelevance vector machine
分类号:
TP75
DOI:
10.11991/yykj.201712002
文献标志码:
A
摘要:
为解决高光谱图像数据维数高、冗余信息较多、容易出现Hughes现象等问题,将改进的连续投影算法应用到高光谱图像降维处理中。改进的连续投影算法在原始算法基础上,分别采用峰度值和偏度值对初始波段的选择进行限制,在较短的时间内获得了少量高效的特征波段,提高了分类性能和处理速度。在AVIRIS数据基础上,对本文提出的算法进行实验仿真,分别采用相关向量机(RVM)和支持向量机(SVM)分类器进行分类处理,并与改进的连续投影算法和蒙特卡罗算法的结果进行比较,实验结果表明改进算法的降维性能更好。
Abstract:
The hyperspectral image data has high dimensionality and much redundant information, which would cause Hughes phenomenon easily. In order to solve this problem, the improved Successive Projection Algorithm (SPA) is applied to the dimension reduction processing of the hyperspectral image data. Based on the initial algorithm, the improved SPA limits the choice of the initial band with the kurtosis and the skewness respectively, and obtains a small number of high-efficiency characteristic bands in a short time, which improves the classification performance and processing speed. Using the AVIRIS data, the algorithm proposed in this paper is simulated by the relevance vector machine (RVM) and support vector machine (SVM) classifier respectively. The experimental results show that SPA performs the Monte Carlo algorithm in improving the dimension reduction performance.

参考文献/References:

[1] LICCIARDI G, MARPU P R, CHANUSSOT J, et al. Linear versus nonlinear PCA for the classification of hyperspectral data based on the extended morphological profiles[J]. IEEE geoscience and remote sensing letters, 2012, 9(3):447-451.
[2] 王秀朋. 基于投影寻踪的高光谱图像降维算法研究[D]. 西安:西北工业大学, 2006:38-43.
[3] 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]. 中国图像图形学报, 2005, 10(2):218-222
[4] 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
[5] MARTÍNEZ-USÓMARTINEZ-USO A, PLA F, SOTOCA J M, et al. Clustering-based hyperspectral band selection using information measures[J]. IEEE transactions on geoscience and remote sensing, 2007, 45(12):4158-4171.
[6] SU Hongjun, DU Qian, CHEN Genshe, et al. Optimized hyperspectral band selection using particle swarm optimization[J]. IEEE journal of selected topics in applied earth observations and remote sensing, 2014, 7(6):2659-2670.
[7] TIWARI R, HUSAIN M, GUPTA S, et al. Improving ant colony optimization algorithm for data clustering[C]//Proceedings of the International Conference and Workshop on Emerging Trends in Technology. New York, USA, 2010:529-534.
[8] 王立国, 赵亮, 刘丹凤. 基于人工蜂群算法高光谱图像波段选择[J]. 哈尔滨工业大学学报, 2015, 47(11):82-88
[9] CUI Ying, WANG Jiaqi, LIU Shubin, et al. Hyperspectral image feature reduction based on tabu search algorithm[J]. Journal of information hiding and multimedia signal processing, 2015, 6(1):154-162.
[10] 崔颖, 宋国娇, 陈立伟, 等. 基于烟花算法降维的高光谱图像分类[J]. 华南理工大学学报:自然科学版, 2017, 45(3):20-28
[11] ARAÚJO M C U, SALDANHA T C B, GALVAO R K H, et al. The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J]. Chemometrics and intelligent laboratory systems, 2001, 57(2):65-73.
[12] FILHO H A D, GALVÃO R K H, ARAÚJO M C U, et al. A strategy for selecting calibration samples for multivariate modelling[J]. Chemometrics and intelligent laboratory systems, 2004, 72(1):83-91.
[13] 郝勇, 孙旭东, 王豪. 基于改进连续投影算法的光谱定量模型优化[J]. 江苏大学学报:自然科学版, 2013, 34(1):49-53
[14] 陈斌, 孟祥龙, 王豪. 连续投影算法在近红外光谱校正模型优化中的应用[J]. 分析测试学报, 2007, 26(1):66-69
[15] 寻丽娜, 方勇华. 基于投影寻踪的高光谱图像目标检测算法[J]. 光子学报, 2006, 35(10):1584-1588
[16] 田禹. 基于偏度和峰度的正态性检验[D]. 上海:上海交通大学, 2012.

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备注/Memo

备注/Memo:
收稿日期:2017-12-07。
基金项目:国家自然科学基金项目(61675051);教育部博士点基金项目(20132304110007)
作者简介:崔颖(1979-),女,副教授,博士;王雪婷(1993-),女,硕士研究生
通讯作者:崔颖,E-mail:cuiying@hrbeu.edu.cn
更新日期/Last Update: 2018-09-04