[1]江 楠,张国明,王俊淑,等.融合低秩和形态学的高光谱影像特征提取[J].南京师范大学学报(工程技术版),2020,20(02):052-58.[doi:10.3969/j.issn.1672-1292.2020.02.008]
　Jiang Nan,Zhang Guoming,Wang Junshu,et al.Feature Extraction of Hyperspectral Remote Sensing ImageBased on Low Rank and Morphology[J].Journal of Nanjing Normal University(Engineering and Technology),2020,20(02):052-58.[doi:10.3969/j.issn.1672-1292.2020.02.008]
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融合低秩和形态学的高光谱影像特征提取
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
20卷
期数:
2020年02期
页码:
052-58
栏目:
测绘科学与技术
出版日期:
2020-05-15

文章信息/Info

Title:
Feature Extraction of Hyperspectral Remote Sensing ImageBased on Low Rank and Morphology
文章编号:
1672-1292(2020)02-0052-07
作者:
江 楠1; 张国明2; 王俊淑3; 韦玉春3
(1.生态环境部珠江流域南海海域生态环境监督管理局,广东 广州 510611)(2.江苏省卫生统计信息中心,江苏 南京 210008)(3.南京师范大学虚拟地理环境教育部重点实验室,江苏 南京 210023)
Author(s):
Jiang Nan1; Zhang Guoming2; Wang Junshu3; Wei Yuchun3
(1.Ecology and Environment Administration for Pearl River Basin and South China Sea,Ministry of Ecology and Environment,Guangzhou 510611,China)(2.Health Statistics and Information Center of Jiangsu Province,Nanjing 210008,China)(3.Key Laboratory for Vir
关键词:
高光谱遥感影像; 形态学; 低秩; 特征提取
Keywords:
hyperspectral remote sensing image; morphology; low rank; feature extraction
分类号:
TP751.1
DOI:
10.3969/j.issn.1672-1292.2020.02.008
文献标志码:
A
摘要:
高光谱遥感影像具有较高的光谱分辨率,能够精细刻画地物的反射光谱,具有很高的地物分类与识别能力. 但高维波段之间通常具有较高的相关性,冗余度高,为影像处理和分析带来负担. 针对高光谱影像特点的特征提取和选择为有效提取信息提供了保障. 提出一种融合低秩和形态学的特征提取方法(MSEMP),利用低秩来精简高光谱影像中的冗余信息,获取秩最小的光谱紧致表达,并在此基础上利用多形态多尺度结构元素提取形态学剖面,获取影像空间特征. 实验对AVIRIS和ROSIS传感器的两组数据进行测试,通过MSEMP提取特征后进行分类实验,可以获得较高的分类结果,证明了低秩和形态学相结合的特征提取方法的有效性.
Abstract:
Hyperspectral remote sensing images with high spectral resolution can describe the reflection spectrum of ground objects in detail,and represent a good ability to classify and identify the ground objects. However,there is usually a high correlation and redundancy among bands,which brings burden to image processing and analysis. Feature extraction and selection of hyperspectral images provide a guarantee for the effective information extraction. In this paper,a feature extraction method,MSEMP,which integrates low rank and morphological profiles is proposed. Low-rank is utilized to simplify the redundant information and obtain the spectral compact expression with the minimum rank of hyperspectral data. Based on low rank representation of hyperspectral image,morphological profiles are extracted by using multi-shaped and multi-scale elements. The proposed algorithm is tested on AVIRIS and ROSIS data,and experimental results show that the classification accuracy based on MSEMP is higher compared with other methods. It indicates that MSEMP is an efficient feature extraction method.

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备注/Memo

备注/Memo:
收稿日期:2019-05-08.
基金项目:国家自然科学基金项目(41471283)、江苏省自然科学基金项目(BK20171037)、江苏省高校自然科学研究面上项目(17KJB420003).
通讯作者:王俊淑,博士,实验师,研究方向:大数据、数据挖掘、遥感图像处理. E-mail:jlsdwjs@126.com
更新日期/Last Update: 2020-05-15