[1]房森,焦淑红.结合分层和ADMM的高光谱图像解混方法[J].应用科技,2020,47(3):46-50.[doi:10.11991/yykj.201908011]
 FANG Sen,JIAO Shuhong.Hyperspectral image unmixing method combining hierarchy and alternating direction method of multipliers (ADMM)[J].Applied science and technology,2020,47(3):46-50.[doi:10.11991/yykj.201908011]
点击复制

结合分层和ADMM的高光谱图像解混方法(/HTML)
分享到:

《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年3期
页码:
46-50
栏目:
现代电子技术
出版日期:
2020-07-05

文章信息/Info

Title:
Hyperspectral image unmixing method combining hierarchy and alternating direction method of multipliers (ADMM)
作者:
房森 焦淑红
哈尔滨工程大学 信息与通信工程学院,黑龙江 哈尔滨 150001
Author(s):
FANG Sen JIAO Shuhong
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
混合像元端元丰度线性模型乘子交替方向法解混光谱变异多端元
Keywords:
mixed pixelendmemberabundancelinear modelalternating direction method of multipliersunmixingspectral variationmultiple endmembers
分类号:
TP751
DOI:
10.11991/yykj.201908011
文献标志码:
A
摘要:
高光谱图像有较高的光谱分辨率,但是单个像元覆盖的面积比较大,导致单个像元中出现多于一种地物的现象,即混合像元。混合像元的存在严重影响了高光谱数据的后续利用。高光谱图像解混技术的目的就是将混合像元中存在的地物种类(端元)以及各个地物种类所对应的比例(丰度)精确地表示出来。高光谱数据覆盖的范围比较大,不可避免存在端元变异的现象。为了应对端元变异现象,利用扩展的线性混合模型对高光谱数据进行建模。在基于分层解混技术的基础上,利用乘子交替方向法对其进行优化。实验结果表明,解混效果得到提升。
Abstract:
Hyperspectral image has a higher spectral resolution, but the area covered by a single pixel is relatively large, resulting in more than one material exist in a single pixel, called a mixed pixel. The presence of mixed pixels severely affects the subsequent use of hyperspectral data. The purpose of hyperspectral image unmixing technique is to determine the materials (endmembers) present in the mixed pixels and their corresponding proportions (abundance). Because the coverage of hyperspactral data is relatively large, the phenomenon of endmember variation exists inevitably. In order to take endmember variation into consideration, an extended linear mixed model is used to describe the hyperspectral data. Based on the hierarchical unmixing technique, the alternating direction method of multipliers is used to optimize the result of unmixing. The experimental results show that the unmixing effect has been greatly improved.

参考文献/References:

[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.
[2] 王立国, 赵春晖. 高光谱图像处理技术[M]. 北京: 国防工业出版社, 2013.
[3] 李宏俏. 高光谱图像的光谱解混模型与算法研究[D]. 成都: 电子科技大学, 2017.
[4] 邹丽. 高光谱图像混合像元解混技术研究[D]. 锦州: 辽宁工业大学, 2018.
[5] 袁静, 章毓晋, 高方平. 线性高光谱解混模型综述[J]. 红外与毫米波学报, 2018, 37(5): 553-571
[6] ZARE A, HO K C. Endmember variability in hyperspectral analysis: addressing spectral variability during spectral unmixing[J]. IEEE signal processing magazine, 2014, 31(1): 95-104.
[7] SOMERS B, ASNER G P, TITS L, et al. Endmember variability in spectral mixture analysis: a review[J]. Remote sensing of environment, 2011, 115(7): 1603-1616.
[8] DRUMETZ L, VEGANZONES M A, HENROT S, et al. Blind hyperspectral unmixing using an extended linear mixing model to address spectral variability[J]. IEEE transactions on image processing, 2016, 25(8): 3890-3905.
[9] 崔士玲. 多端元高光谱图像解混算法研究[D]. 哈尔滨: 哈尔滨工程大学, 2015.
[10] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and trends in machine learning, 2010, 3(1): 1-122.
[11] ROBERTS D A, GARDNER M, CHURCH R, et al. Mapping chaparral in the Santa Monica mountains using multiple endmember spectral mixture models[J]. Remote sensing of environment, 1998, 65(3): 267-279.
[12] 王可. 多端元光谱混合分析算法研究[D]. 成都: 电子科技大学, 2015.
[13] 崔媛. 高光谱图像混合像元分解技术研究[D]. 沈阳: 沈阳航空航天大学, 2016.
[14] BIOUCAS-DIAS J M, PLAZA A. an overview on hyperspectral unmixing: geometrical, statistical, and sparse regression based approaches[C]// Proceedings of 2011 IEEE International Geoscience and Remote Sensing Symposium. Vancouver, BC, Canada, 2011: 1135-1138.
[15] AMMANOUIL R, FERRARI A, RICHARD C, et al. Blind and fully constrained unmixing of hyperspectral images[J]. IEEE transactions on image processing, 2014, 23(12): 5510-5518.

相似文献/References:

[1]房森,焦淑红.分组寻优的多端元高光谱图像解混方法[J].应用科技,2019,46(06):20.[doi:10.11991/yykj.201903005]
 FANG Sen,JIAO Shuhong.Multi-endmember hysperspectral image unmixing based on group optimization[J].Applied science and technology,2019,46(3):20.[doi:10.11991/yykj.201903005]
[2]韩月,康维新,李慧.先验信息约束NMF的高光谱解混[J].应用科技,2019,46(04):77.[doi:10.11991/yykj.201809022]
 HAN Yue,KANG Weixin,LI Hui.Hyperspectral unmixing of nonnegative matrix factorization with prior information[J].Applied science and technology,2019,46(3):77.[doi:10.11991/yykj.201809022]

备注/Memo

备注/Memo:
收稿日期:2019-08-14。
作者简介:房森,男,硕士研究生;焦淑红,女,教授,博士生导师
通讯作者:房森,E-mail:Sen_Fang2019@163.com
更新日期/Last Update: 2020-08-05