空谱协同多尺度顶点成分分析的高光谱影像端元提取

doi:10.11947/j.AGCS.2022.20210718

测绘学报 ›› 2022, Vol. 51 ›› Issue (4): 587-598.doi: 10.11947/j.AGCS.2022.20210718

• 同济大学测绘学科创建90周年 • 上一篇下一篇

空谱协同多尺度顶点成分分析的高光谱影像端元提取

孙伟伟¹, 常明会¹, 孟祥超², 杨刚¹, 任凯¹

1. 宁波大学地理与空间信息技术系, 浙江宁波 315211;
2. 宁波大学信息科学与工程学院, 浙江宁波 315211

收稿日期:2021-10-26 修回日期:2022-03-21 发布日期:2022-04-24
通讯作者: 孟祥超 E-mail:mengxiangchao@nbu.edu.cn
作者简介:孙伟伟(1985-),男,博士,教授,研究方向为海岸带高光谱遥感。.E-mail:sunweiwei@nbu.edu.cn
基金资助:
国家自然科学基金（42122009；41971296；41671342；41801256；41801252）；浙江省自然科学基金（LR19D010001；LQ18D010001）；浙江省自然资源厅项目（2021-30；2021-31）；宁波市科技创新“2025”重大专项（2021Z107）

Spatial-spectral collaborative multi-scale vertex component analysis for hyperspectral image endmember extraction

SUN Weiwei¹, CHANG Minghui¹, MENG Xiangchao², YANG Gang¹, REN Kai¹

1. Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China;
2. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China

Received:2021-10-26 Revised:2022-03-21 Published:2022-04-24
Supported by:
The National Natural Science Foundation of China (Nos. 42122009; 41971296; 41671342; 41801256; 41801252); The Natural Science Foundation of Zhejiang Province (Nos. LR19D010001; LQ18D010001); The Project of Zhejiang Provincial Department Natural Resources(Nos. 2021-30; 2021-31); Ningbo Science and Technology Innovation “2025” Major Special Project(No. 2021Z107)

摘要/Abstract

摘要： 针对顶点成分分析方法无法实现复杂地表环境下的高光谱影像端元精确提取问题,提出了一种基于空谱协同多尺度顶点成分分析的端元提取方法,通过影像空谱特征融合和聚类分割,对不同分辨率空间尺度下的分割影像进行端元协同提取,并考虑噪声对影像端元提取精度的影响,提升端元提取的精度。首先,对影像进行预处理,采用低秩矩阵分解去除噪声。其次,对高光谱影像进行空谱多特征提取,利用多特征融合和K-means算法进行聚类分割,获取地物分布的空间异质性信息,提升后续端元提取的精度。然后,对高分辨率影像空间降采样,利用顶点成分分析方法对降采样后的低分辨率分割图像进行端元提取,并利用坐标映射寻找高分辨率影像中的相应端元,利用光谱角来判定是否为纯端元。最后,遍历上述方法至所有分割影像以获取最终的端元集合。使用模拟数据和真实的高分五号高光谱数据对提出的方法进行实验验证。实验结果表明,空谱协同多尺度顶点成分分析方法可提取高精度的纯净端元,且计算效率较高。

关键词: 光谱端元, 高光谱遥感, 空谱协同, 多尺度顶点成分分析

Abstract: Current endmember extraction methods cannot accurately extract the endmembers of complicated ground features, and therefore this paper proposed a spatial-spectral collaborative multi-scale vertex component analysis (VCA) method. Hyperspectral images are firstly jointly clustered and segmented based on multi-feature fusion using spectral features, texture features, and shape features, which makes full use of the spatial heterogeneity information of ground features. Then, multi-scale low-rank matrix decomposition is used to decompose the segmented images and reduce the influence of noise on endmember extraction. Meanwhile, VCA is used to extract endmembers from low-resolution images, coordinate mapping is implemented to search these endmembers of high-resolution images, and vertex component analysis is used to extract endmember from low resolution image. After that, coordinate mapping is used to ferret about the corresponding endmembers in the high-resolution image, and the spectral angle between them is calculated to help accurately decide the pure endmembers. Finally, the proposed method is traversed into all segmented images to obtain the final pure endmembers. The proposed method is verified experimentally by using simulated and real GF-5 hyperspectral data. Experimental results show that the CVCA method can extract high-precision pure endmembers and has high calculation efficiency.

Key words: endmember, hyperspectral, spatial-spectral collaboration, vertex component analysis

中图分类号:

P227

孙伟伟, 常明会, 孟祥超, 杨刚, 任凯. 空谱协同多尺度顶点成分分析的高光谱影像端元提取[J]. 测绘学报, 2022, 51(4): 587-598.

SUN Weiwei, CHANG Minghui, MENG Xiangchao, YANG Gang, REN Kai. Spatial-spectral collaborative multi-scale vertex component analysis for hyperspectral image endmember extraction[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(4): 587-598.

参考文献

[1] 孙伟伟,杨刚,陈超,等. 中国地球观测遥感卫星发展现状及文献分析[J]. 遥感学报,2020,24(5): 479-510. SUN Weiwei,YANG Gang,CHEN Chao,et al. Development status and literature analysis of China's earth observation and remote sensing satellites [J]. National Remote Sensing Bulletin,2020,24 (5): 479-510.
[2] 孟祥超,孙伟伟. 基于多分辨率分析的GF-5和GF-1遥感影像空—谱融合[J]. 遥感学报,2020, 24(4): 379-387. MENG Xiangchao,SUN Weiwei. Spatial spectral fusion of GF-5 and GF-1 remote sensing images based on multi-resolution analysis [J]. National Remote Sensing Bulletin,2020,24 (4): 379-387.
[3] SUN Weiwei,MA Jun,YANG Gang. A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery[J]. ISPRS Journal of Photogrammetry Remote Sensing,2017(128): 27-39.
[4] 韩竹,高连如,张兵,等. 高分五号高光谱图像自编码网络非线性解混[J]. 遥感学报,2019,24(4): 388-400. HAN Zhu,GAO Lianru,ZHANG Bing,et al.Nonlinear unmixing of GF-5 hyperspectral image self coding network [J].National Remote Sensing Bulletin,2019,24 (4): 388-400.
[5] XU Xiang,LI Jun,WU Changshan,et al. Regional clustering -based spatial preprocessing for hyperspectral unmixing[J]. Remote Sensing of Environment,2018(204): 333-346.
[6] 丁海勇,史文中. 利用卡方分布改进N-FINDR端元提取算法[J]. 遥感学报,2012,17(1): 122-137. DING Haiyong,SHI Wenzhong.Improved N-FINDR endmember extraction algorithm using chi square distribution [J]. National Remote Sensing Bulletin,2012,17 (1): 122-137.
[7] 田玉刚,杨贵. 端元快速提取的光谱梯度特征搜索法[J]. 测绘学报,2015, 44(2): 214-219,227. DOI: 10.11947/j.AGCS.2015.20130392. TIAN Yugang,YANG Gui.Spectral gradient feature search method for rapid extraction of endmembers [J]. Acta Geodaetica et Cartographica Sinica,2015,44 (2): 214-219, 227. DOI: 10.11947/j.AGCS.2015.20130392.
[8] 王丽,王威,刘勃妮.顶点成分分析法应用于高光谱图像端元提取[J].信息技术,2022(1):37-43.DOI:10.13274/j.cnki.hdzj.2022.01.008. WANG Li,WANG Wei,LIU Boni. Application of vertex component analysis to endmember extraction of hyperspectral images [J]. Information Technology,2022(1): 37-43. DOI:10.13274/ j.cnki. hdzj. 2022.01.008.
[9] 袁博. 基于混合像元空间与谱间相关性模型的NMF线性盲解混[J]. 测绘学报,2019,48(9): 1151-1160.DOI: 10.11947/j.AGCS.2019.20180054. YUAN Bo. NMF linear blind unmixing based on the spatial and spectral correlation model of mixed pixels [J]. Acta Geodaetica et Cartographica Sinica,2019,48 (9): 1151-1160. DOI: 10.11947/j.AGCS.2019.20180054.
[10] 徐君,王彩玲,王丽. 采用PPI算法改进的一种数学形态学端元提取方法 [J]. 测绘学报,2019, 48(8): 996-1003.DOI:10.11947/j.AGCS.2019.20180475. XU Jun,WANG Cailing, WANG Li. A mathematical morphology endmember extraction method improved by PPI algorithm [J]. Acta Geodaetica et Cartographica Sinica,2019,48 (8): 996-1003. DOI:10.11947/j.AGCS.2019.20180475.
[11] 李二森,朱书龙,周晓明,等. 高光谱图像端元提取算法研究进展与比较 [J]. 遥感学报,2011,15(4): 659-679. LI Ersen,ZHU Shulong,ZHOU Xiaoming,et al. Research progress and comparison of endmember extraction algorithms for hyperspectral images [J]. National Remote Sensing Bulletin,2011,15 (4): 659-679.
[12] 程熙,沈占锋,骆剑承,等. 利用混合光谱分解与SVM估算不透水面覆盖率[J]. 遥感学报, 2011,15(6): 1235-1247. CHENG Xi,SHEN Zhanfeng,LUO Jiancheng,et al. Estimation of impervious surface coverage using mixed spectral decomposition and SVM [J]. National Remote Sensing Bulletin, 2011,15 (6): 1235-1247.
[13] 方俊龙,郭宝峰,沈宏海,等. 一种改进的基于自动形态学的端元提取算法[J]. 激光技术,2017,41(1): 106-112. FANG Junlong,GUO Baofeng,SHEN Honghai,et al. An improved endmember extraction algorithm based on automatic morphology [J]. Laser Technology,2017,41 (1): 106-112.
[14] 郭诗韵. 一种基于空谱协同的高光谱端元提取方法研究[J]. 地理空间信息,2020,18(2): 34-37. GUO Shiyun. Research on a hyperspectral endmember extraction method based on spatial spectrum cooperation [J]. Geospatial Information, 2020,18 (2): 34-37.
[15] 王忠美,杨晓梅,顾行发. 张量组稀疏表示的高光谱图像去噪算法[J]. 测绘学报, 2017,46(5): 614-622.DOI: 10.11947/j.AGCS.2017.20150403. WANG Zhongmei,YANG Xiaomei, GU Xingfa. Hyperspectral Image denoising based on tensor group sparse representation[J]. Acta Geodaetica et Cartographica Sinica,2017,46(5): 614-622. DOI: 10.11947/j.AGCS.2017.20150403.
[16] EKLUNDH L,SINGH A. A comparative analysis of standardised and unstandardised principal components analysis in remote sensing[J]. International Journal of Remote Sensing,1993,14(7): 1359-1370.
[17] XU Qian,VARADARAJAN S,CHAKRABARTI C,et al. A distributed canny edge detector: algorithm and FPGA implementation[J]. IEEE Transactions on Image Processing,2014,23(7): 2944-2960.
[18] SATPATHY A,XU Dongjiang,ENG H-L. LBP-based edge-texture features for object recognition[J]. IEEE Transactions on Image Processing,2014,23(5): 1953-1964.
[19] SHAH D,ZAVERI T. Hyperspectral endmember extraction using band quality[C]//Proceedings of 2019 IEEE India Council International Conference. Rajkot, India: IEEE, 2019: 1-4.
[20] SHAH D,ZAVERI T,TRIVEDI Y N,et al. Entropy-based convex set optimization for spatial-spectral endmember extraction from hyperspectral images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13(8):4200-4213.
[21] KUMAR U,CRISTINA M,NEMANI R,et al. Sparse unmixing via variable splitting and augmented Lagrangian for vegetation and urban area classification using Landsat data[J].ISPRS-International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2015, 40(7):59-65.

空谱协同多尺度顶点成分分析的高光谱影像端元提取

Spatial-spectral collaborative multi-scale vertex component analysis for hyperspectral image endmember extraction

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价

[1]	魏立飞, 余铭, 钟燕飞, 袁自然, 黄灿. 空-谱融合的条件随机场高光谱影像分类方法[J]. 测绘学报, 2020, 49(3): 343-354.
[2]	黄鸿, 石光耀, 段宇乐, 张丽梅. 加权空-谱联合保持嵌入的高光谱遥感影像降维方法[J]. 测绘学报, 2019, 48(8): 1014-1024.
[3]	李敏, 朱国康, 张学武, 范新南, 李普煌. 基于多孔径映射的高光谱异常检测算法[J]. 测绘学报, 2016, 45(10): 1222-1230.
[4]	王俊淑, 江南, 张国明, 李杨, 吕恒. 融合光谱-空间信息的高光谱遥感影像增量分类算法[J]. 测绘学报, 2015, 44(9): 1003-1013.
[5]	张涛, 刘军, 杨可明, 罗文杉, 张育育. 结合Gram-Schmidt变换的高光谱影像谐波分析融合算法[J]. 测绘学报, 2015, 44(9): 1042-1047.
[6]	丰明博刘学赵冬. 多/高光谱遥感图像的投影和小波融合算法[J]. 测绘学报, 2014, 43(2): 158-163.
[7]	石茜,杜博,张良培. 一种基于局部判别正切空间排列的高光谱遥感影像降维方法[J]. 测绘学报, 2012, 41(3): 0-416.
[8]	谭琨,杜培军. 基于再生核Hilbert空间的小波核函数支持向量机的高光谱遥感影像分类[J]. 测绘学报, 2011, 40(2): 142-147.