[1]崔颖,王恒,朱海峰.结构张量全变差再优化稀疏高光谱解混[J].哈尔滨工程大学学报,2020,41(7):1087-1093.[doi:10.11990/jheu.201901096]
 CUI Ying,WANG Heng,ZHU Haifeng.Structural-tensor total-variation re-optimization sparse hyperspectral unmixing[J].hebgcdxxb,2020,41(7):1087-1093.[doi:10.11990/jheu.201901096]
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结构张量全变差再优化稀疏高光谱解混(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
41
期数:
2020年7期
页码:
1087-1093
栏目:
出版日期:
2020-07-05

文章信息/Info

Title:
Structural-tensor total-variation re-optimization sparse hyperspectral unmixing
作者:
崔颖 王恒 朱海峰
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):
CUI Ying WANG Heng ZHU Haifeng
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
关键词:
高光谱遥感光谱解混稀疏解混空间信息结构张量全变差重建误差解混成功率
分类号:
TN751.1
DOI:
10.11990/jheu.201901096
文献标志码:
A
摘要:
为改善全变差正则化变量分离与增广拉格朗日(SUnSAL-TV)算法求解的丰度存在过平滑与边界模糊的现象,本文提出结构张量全变差(STV)再优化的稀疏解混算法(SUnSAL-TV-STV),用STV正则项校正SUnSAL-TV算法求解的丰度矩阵。本文在合成数据集与真实高光谱数据集上进行算法仿真,合成数据实验结果表明:本文算法与其他算法相比,解混重建误差提高0.01~0.03且具有最高的解混成功率,通过对真实数据解混丰度图的观察,本文算法较好地修复了SUnSAL-TV算法求解丰度图的过平滑与边界模糊现象。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2019-01-31。
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,女,副教授;朱海峰,男,博士.
通讯作者:朱海峰,E-mail:zhuhaifeng@hrbeu.edu.cn.
更新日期/Last Update: 2020-08-15