[1]陈 欣,粘永健,王忠良.基于线性混合模型的高光谱图像分布式压缩感知[J].红外技术,2019,41(8):758-763.[doi:10.11846/j.issn.1001_8891.2019080011]
 CHEN Xin,NIAN Yongjian,WANG Zhongliang.Distributed Compressive Sensing for Hyperspectral Imaging Based on Linear Mixing Model [J].Infrared Technology,2019,41(8):758-763.[doi:10.11846/j.issn.1001_8891.2019080011]
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基于线性混合模型的高光谱图像分布式压缩感知
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
41卷
期数:
2019年第8期
页码:
758-763
栏目:
出版日期:
2019-08-21

文章信息/Info

Title:
Distributed Compressive Sensing for Hyperspectral Imaging
Based on Linear Mixing Model
文章编号:
1001-8891(2019)08-0758-06
作者:
陈 欣1粘永健2王忠良3
1. 重庆工程学院 软件学院,重庆 400056;2. 陆军军医大学(第三军医大学)生物医学工程与影像医学系,重庆 400038;
3. 铜陵学院 电气工程学院,安徽 铜陵 244061
Author(s):
CHEN Xin1NIAN Yongjian2WANG Zhongliang3
1. 重庆工程学院 软件学院,重庆 400056;2. 陆军军医大学(第三军医大学)生物医学工程与影像医学系,重庆 400038;
3. 铜陵学院 电气工程学院,安徽 铜陵 244061
关键词:
分布式压缩感知高光谱图像线性混合模型解混
Keywords:
分布式压缩感知高光谱图像线性混合模型解混
分类号:
TP751
DOI:
10.11846/j.issn.1001_8891.2019080011
文献标志码:
A
摘要:
为了实现高光谱图像的有效压缩采样与重构,对分布式压缩采样的高光谱数据应用线性混合模型进行重构。首先,在图像采集阶段,针对高光谱图像的空谱特性,应用分布式压缩采样策略对高光谱数据进行采集;在数据重构阶段,应用高光谱图像的线性混合模型假设,先对压缩数据进行端元数目的估计,再利用估计的端元数来估计丰度矩阵,根据端元特征信号的稀疏性质提取端元矩阵,从而重构出原始的高光谱数据,抛弃了压缩感知重构算法中高计算复杂性的欠定问题求解。实验结果表明:在压缩采样数据为总数据的20%时,重构的平均信噪比比压缩投影主成分分析算法提高了15 dB以上,同时该方法还便于获得端元和丰度信息。所设计的压缩感知方案采样方式简单,重构速度快、精度高,可应用于星载或机载的高光谱压缩感知成像。
Abstract:
为了实现高光谱图像的有效压缩采样与重构,对分布式压缩采样的高光谱数据应用线性混合模型进行重构。首先,在图像采集阶段,针对高光谱图像的空谱特性,应用分布式压缩采样策略对高光谱数据进行采集;在数据重构阶段,应用高光谱图像的线性混合模型假设,先对压缩数据进行端元数目的估计,再利用估计的端元数来估计丰度矩阵,根据端元特征信号的稀疏性质提取端元矩阵,从而重构出原始的高光谱数据,抛弃了压缩感知重构算法中高计算复杂性的欠定问题求解。实验结果表明:在压缩采样数据为总数据的20%时,重构的平均信噪比比压缩投影主成分分析算法提高了15 dB以上,同时该方法还便于获得端元和丰度信息。所设计的压缩感知方案采样方式简单,重构速度快、精度高,可应用于星载或机载的高光谱压缩感知成像。

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2018-03-26;修订日期:2018-07-09.
作者简介:陈欣(1982-),女,重庆合川人,副教授,硕士,主要从事计算机应用教研工作。
通信作者:王忠良(1980-),男,安徽舒城人,副教授,博士,主要从事遥感图像处理。E-mail:asdwzl@hotmail.com。
基金项目:安徽省高等学校自然科学研究重点项目(KJ2016A884);安徽省高等学校省级自然科学研究项目(KJ2013B298);安徽省高等学校省级质量工程项目(2016zy126);重庆市基础科学与前沿技术一般项目(cstc2016jcyjA0539)。
更新日期/Last Update: 2019-08-20