面向线性光谱混合分解的邻域像元集螺线型构建方法

doi:10.11947/j.AGCS.2017.20170109

测绘学报 ›› 2017, Vol. 46 ›› Issue (11): 1841-1849.doi: 10.11947/j.AGCS.2017.20170109

面向线性光谱混合分解的邻域像元集螺线型构建方法

刘博宇^1,2, 陈军², 邢华桥^2,3, 武昊², 张俊²

1. 吉林大学地球探测科学与技术学院, 吉林长春 130026;
2. 国家基础地理信息中心, 北京 100830;
3. 中国矿业大学(北京)地球科学与测绘工程学院, 北京 100083

收稿日期:2017-03-09 修回日期:2017-07-12 出版日期:2017-11-20 发布日期:2017-12-05
作者简介:刘博宇(1987-),男,博士生,研究方向为遥感时空数据融合与地表覆盖制图。E-mail:liuby10@mails.jlu.edu.cn
基金资助:
国家自然科学基金重点项目（41231172）

A Spiral-based Construction of Adjacent Pixel Sets for Linear Spectral Unmixing

LIU Boyu^1,2, CHEN Jun², XING Huaqiao^2,3, WU Hao², ZHANG Jun²

1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China;
2. National Geomatics Center of China, Beijing 100830, China;
3. Geoscience and Surveying Engineering College, China University of Mining and Technology(Beijing), Beijing 100083, China

Received:2017-03-09 Revised:2017-07-12 Online:2017-11-20 Published:2017-12-05
Supported by:
The State Key Program of National Natural Science Foundation of China (No. 41231172)

摘要/Abstract

摘要：

高时间分辨率遥感影像在地表景观破碎区域易形成混合像元，难以发挥其高时间维度优势。现有方式多是基于线性光谱混合模型，借助邻域像元所构成的像元集合组成线性方程组，求出组分光谱值的最小二乘解，提高其空间分辨率。然而，现有方法依赖窗口形式来构建邻域像元集合，在某些区域易造成方程组无解的欠定问题。本文在分析其问题原因的基础上，引入阿基米德螺线代替传统的矩形窗口，对邻域各像元依次遍历，构建空间邻近、组分相近的邻域像元集合来解决该问题。在GlobeLand 30数据上的试验表明，螺线型构建方法对5种混合尺度上多种类型地物均具有稳定的精度，与传统窗口构建方法相比，可从构建邻域像元集合方面将总体理论精度提高2%，分解结果精度提高近1个数量级。

关键词: 线性光谱混合分解, 混合像元, 邻域像元, 螺线, 窗口

Abstract:

The problem of spectral mixing in fragmented landscape prevents the potentiality of high temporal resolution image from land surface detail dynamic monitoring. The general way is unmixing the spectrum to components based on linear spectral mixing model,with the aid of neighborhood pixels and the least squares method. However,constructing the neighborhood pixel set by a window leads to the underdetermined problem in some areas. This paper analyses the cause of the problem,and introduces spiral to construct optimal neighborhood pixel set as a solution. Experiment on GlobeLand 30 shows that the spiral method has a good applicability for each land surface type. Compared with the traditional method,the overall accuracy can be enhanced by 2%,the accuracy of unmixed results can be improved by nearly one order of magnitude.

Key words: linear spectral unmixing, mixed pixel, adjacent pixel, spiral, window

中图分类号:

P237

刘博宇, 陈军, 邢华桥, 武昊, 张俊. 面向线性光谱混合分解的邻域像元集螺线型构建方法[J]. 测绘学报, 2017, 46(11): 1841-1849.

LIU Boyu, CHEN Jun, XING Huaqiao, WU Hao, ZHANG Jun. A Spiral-based Construction of Adjacent Pixel Sets for Linear Spectral Unmixing[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(11): 1841-1849.

参考文献

[1] 陈军,陈晋,廖安平,等.全球30 m地表覆盖遥感制图的总体技术[J].测绘学报,2014,43(6):551-557.DOI:10.13485/j.cnki.11-2089.2014.0089. CHEN Jun,CHEN Jin,LIAO Anping,et al.Concepts and Key Techniques for 30 m Global Land Cover Mapping[J].Acta Geodaetica et Cartographica Sinica,2014,43(6):551-557.DOI:10.13485/j.cnki.11-2089.2014.0089.
[2] CHEN Xuehong,YANG Dedi,CHEN Jin,et al.An Improved Automated Land Cover Updating Approach by Integrating with Downscaled NDVI Time Series Data[J].Remote Sensing Letters,2015,6(1):29-38.
[3] CHEN Jun,CHEN Jin,LIAO Anping,et al.Global Land cover Mapping at 30 m Resolution:A POK-based Operational Approach[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,103(1):7-27.
[4] GEVAERT C M,GARCíA-HARO F J.A Comparison of STARFM and an Unmixing-based Algorithm for Landsat and MODIS Data Fusion[J].Remote Sensing of Environment,2015,156(1):34-44.
[5] GAO Feng,MASEK J,SCHWALLER M,et al.On the Blending of the Landsat and MODIS Surface Reflectance:Predicting Daily Landsat Surface Reflectance[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(8):2207-2218.
[6] PERERA K,TSUCHIYA K.Experiment for Mapping Land Cover and It's Change in Southeastern Sri Lanka Utilizing 250 m Resolution MODIS Imageries[J].Advances in Space Research,2009,43(9):1349-1355.
[7] SAVTCHENKO A,OUZOUNOV D,AHMAD S,et al.Terra and Aqua MODIS Products Available from NASA GES DAAC[J].Advances in Space Research,2004,34(4):710-714.
[8] 吴波,熊助国.基于光谱最佳尺度分割特征的高光谱混合像元分解[J].测绘学报,2012,41(2):205-212. WU Bo,XIONG Zhuguo.Unmixing of Hyperspectral Mixture Pixels Based on Spectral Multiscale Segemented Features[J].Acta Geodaetica et Cartographica Sinica,2012,41(2):205-212.
[9] ASNER G P,WESSMAN C A,PRIVETTE J L.Unmixing the Directional Reflectances of AVHRR Sub-pixel Landcovers[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(4):868-878.
[10] 李慧,张金区,曹阳,等.端元可变非线性混合像元分解模型[J].测绘学报,2016,45(1):80-86.DOI:10.11947/j.AGCS.2016.20140520. LI Hui,ZHANG Jinqu,CAO Yang,et al.Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets[J].Acta Geodaetica et Cartographica Sinica,2016,45(1):80-86.DOI:10.11947/j.AGCS.2016.20140520.
[11] 张良培,沈焕锋.遥感数据融合的进展与前瞻[J].遥感学报,2016,20(5):1050-1061. ZHANG Liangpei,SHEN Huanfeng.Progress and Future of Remote Sensing Data Fusion[J].Journal of Remote Sensing,2016,20(5):1050-1061.
[12] 邬明权,王洁,牛铮,等.融合MODIS与Landsat数据生成高时间分辨率Landsat数据[J].红外与毫米波学报,2012,31(1):80-84. WU Mingquan,WANG Jie,NIU Zheng,et al.A Model for Spatial and Temporal Data Fusion[J].Journal of Infrared and Millimeter Waves,2012,31(1):80-84.
[13] RAO Yuhan,ZHU Xiaolin,CHEN Jin,et al.An Improved Method for Producing High Spatial-resolution NDVI Time Series Datasets with Multi-temporal MODIS NDVI Data and Landsat TM/ETM+ Images[J].Remote Sensing,2015,7(6):7865-7891.
[14] 陈晋,马磊,陈学泓,等.混合像元分解技术及其进展[J].遥感学报,2016,20(5):1102-1109. CHEN Jin,MA Lei,CHEN Xuehong,et al.Research Progress of Spectral Mixture Analysis[J].Journal of Remote Sensing,2016,20(5):1102-1109.
[15] 何青,熊桢.基于邻域的混合象元解译[J].北方交通大学学报,1999,23(4):118-121. HE Qing,XIONG Zhen.Neighbor-field-based Mixed Pixel Interpretation[J].Journal of Northern Jiaotong University,1999,23(4):118-121.
[16] ZHUKOV B,OERTEL D,LANZL F,et al.Unmixing-based Multisensor Multiresolution Image Fusion[J].IEEE Transactions on Geoscience and Remote Sensing,1999,37(3):1212-1226.
[17] MASELLI F.Definition of Spatially Variable Spectral Endmembers by Locally Calibrated Multivariate Regression Analyses[J].Remote Sensing of Environment,2001,75(1):29-38.
[18] BUSETTO L,MERONI M,COLOMBO R.Combining Medium and Coarse Spatial Resolution Satellite Data to Improve the Estimation of Sub-pixel NDVI Time Series[J].Remote Sensing of Environment,2008,112(1):118-131.
[19] ZURITA-MILLA R,CLEVERS J G P W,SCHAEPMAN M E.Unmixing-based Landsat TM and MERIS FR Data Fusion[J].IEEE Geoscience and Remote Sensing Letters,2008,5(3):453-457.
[20] 谢登峰,张锦水,孙佩军,等.结合像元分解和STARFM模型的遥感数据融合[J].遥感学报,2016,20(1):62-72. XIE Dengfeng,ZHANG Jinshui,SUN Peijun,et al.Remote Sensing Data Fusion by Combining STARFM and Downscaling Mixed Pixel Algorithm[J].Journal of Remote Sensing,2016,20(1):62-72.
[21] 张建,李宗南,谢静.亚像元定位之邻域像元选取问题研究[J].华中师范大学学报(自然科学版),2013,47(3):404-409. ZHANG Jian,LI Zongnan,XIE Jing.Research on Neighbor Pixels Choosing Issue of the Sub-pixel Mapping[J].Journal of Huazhong Normal University (Natural Sciences),2013,47(3):404-409.
[22] 王明华,杨继绪.阿基米德螺线的性质与应用[J].数学通报,1989(7):11-12. WANG Minghua,YANG Jixu.The Properties and Application of Archimedean Spiral[J].Bulletin des Sciences Mathematics,1989(7):11-12.
[23] 姚建武.螺线与生物体上的拟螺线[J].科学,2004,56(4):61-63. YAO Jianwu.On Spiral and Organism Draft Spiral Research[J].Science,2004,56(4):61-63.
[24] XU Mingming,ZHANG Liangpei,DU Bo.An Image-based Endmember Bundle Extraction Algorithm Using both Spatial and Spectral Information[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(6):2607-2617.
[25] DU Bo,ZHANG Liangpei.A Discriminative Metric Learning Based Anomaly Detection Method[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):6844-6857.
[26] DU Bo,ZHANG Liangpei.Target Detection Based on a Dynamic Subspace[J].Pattern Recognition,2014,47(1):344-358.

面向线性光谱混合分解的邻域像元集螺线型构建方法

A Spiral-based Construction of Adjacent Pixel Sets for Linear Spectral Unmixing

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