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; 陈军; 邢华桥; 武昊; 张俊

doi:10.11947/j.AGCS.2017.20170109

测绘学报 >

2017 , Vol. 46 >Issue 11: 1841 - 1849

DOI: https://doi.org/10.11947/j.AGCS.2017.20170109

摄影测量学与遥感

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 ,
陈军 ,
邢华桥 ,
武昊 ,
张俊

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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

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

收稿日期: 2017-03-09

修回日期: 2017-07-12

网络出版日期: 2017-12-05

基金资助

国家自然科学基金重点项目（41231172）

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

LIU Boyu ,
CHEN Jun ,
XING Huaqiao ,
WU Hao ,
ZHANG Jun

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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 date: 2017-03-09

Revised date: 2017-07-12

Online published: 2017-12-05

Supported by

The State Key Program of National Natural Science Foundation of China (No. 41231172)

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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

本文引用格式

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 . DOI: 10.11947/j.AGCS.2017.20170109

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

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