端元可变非线性混合像元分解模型

doi:10.11947/j.AGCS.2016.20140520

测绘学报 ›› 2016, Vol. 45 ›› Issue (1): 80-86.doi: 10.11947/j.AGCS.2016.20140520

端元可变非线性混合像元分解模型

李慧, 张金区, 曹阳, 王兴芳

华南师范大学计算机学院, 广东广州 510630

收稿日期:2014-10-20 修回日期:2015-07-31 出版日期:2016-01-20 发布日期:2016-01-28
通讯作者: 张金区, jinquzhang@scnu.edu.cn E-mail:jinquzhang@scnu.edu.cn
作者简介:李慧(1980—),女,博士,讲师,研究方向为遥感图像处理,空间信息分析与应用。
基金资助:
国家自然科学基金(41171288);广东省自然科学基金(S2013040016473;S2013010014097)

Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets

LI Hui, ZHANG Jinqu, CAO Yang, WANG Xingfang

Computer School, South China Normal University, Guangzhou 510630, China

Received:2014-10-20 Revised:2015-07-31 Online:2016-01-20 Published:2016-01-28
Supported by:
The National Natural Science Foundation of China (No.41171288);The National Natural Science Foundation of Guangdong Province of China(Nos. S2013040016473;S2013010014097)

摘要/Abstract

摘要： 遥感影像中混合像元普遍存在。端元固定的情况下对混合像元进行分解,很难高精度地识别影像地物。本文基于支持向量机,提出了端元可变的非线性混合像元分解模型。首先,通过构建多个支持向量机获取每个像元的优化端元集,在优化端元集的基础上运用支持向量机与两两配对方法相结合的算法获取像元组分。试验结果表明,本文提出的方法效果优于传统的多端元光谱分解法。

关键词: 遥感影像, 像元分解, 端元变化, 支持向量机, 非线性解混

Abstract: For a given pixel, fractional abundances predicted by spectral mixture analysis (SMA) are most accurate when only the endmembers that comprise it are used. This paper presents a support vector machines (SVM) method to achieve land use/land cover fractions of remote sensing image using two steps: ①defining the optimal per-pixel endmember set, which removes endmembers based on negative fractional abundances generated by SVM method; ②using SVM extended with pairwise coupling (PWC) to output probabilities as the abundance of landscape fractions. The performances of the proposed method were evaluated with the multiple endmember spectral mixture analysis (MESMA) method, which has been widely applied to map land cover for the goodness of the model fitting. The results obtained in this study were validated by real fractions generated from SPOT high resolution geometric (HRG) image. The best classification results were obtained by the proposed method indicated by the lower total mean absolute error, the higher overall accuracy, and the higher kappa. From this study, the proposed method is proved to be effective in obtaining abundance fractions that are physically realistic (sum close to one and nonnegative), and providing valuable application in selecting endmembers that occur within a pixel.

Key words: remote sensing image, pixel unmixing, selective endmember, support vector machines (SVM), nonlinear unmixing

中图分类号:

P237

李慧, 张金区, 曹阳, 王兴芳. 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.

LI Hui, ZHANG Jinqu, CAO Yang, WANG Xingfang. Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(1): 80-86.

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端元可变非线性混合像元分解模型

Nonlinear Spectral Unmixing for Optimizing Per-pixel Endmember Sets

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