Unsupervised Unmixing of Hyperspectral Imagery using the Positive Matrix Factorization

机译：使用正矩阵分解的高光谱影像的无监督分解

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This paper presents an approach for simultaneous determination of endmembers and their abundances in hyperspectral imagery unmixing using a constrained positive matrix factorization (PMF). The algorithm presented here solves the constrained PMF using Gauss-Seidel method. This algorithm alternates between the endmembers matrix updating step and the abundance estimation step until convergence is achieved. Preliminary results using a subset of a HYPERION image taken in SW Puerto Rico are presented. These results show the potential of the proposed method to solve the unsupervised unmixing problem.

机译：本文提出了一种使用约束正矩阵分解（PMF）来同时确定高光谱图像解混合中的端成员及其丰度的方法。本文提出的算法使用高斯-塞德尔方法求解约束PMF。该算法在端成员矩阵更新步骤和丰度估计步骤之间交替，直到实现收敛为止。给出了使用在波多黎各西南部拍摄的HYPERION图像子集的初步结果。这些结果表明了该方法解决无监督解混问题的潜力。

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来源
《Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV》|2006年|P.62470B.1-62470B.9|共9页
会议地点 KissimmeeFL(US)
作者
Yahya M. Masalmah; Miguel Velez-Reyes;
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作者单位

Laboratory for Applied Remote Sensing Image Processing Center for Subsurface Sensing Imaging Systems University of Puerto Rico Mayagueez Campus P.O. Box 9042, Mayagueez, PR 00681-9042;

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会议组织
原文格式 PDF
正文语种 eng
中图分类图像信号处理;
关键词
hyperspectral imagery; spectral unmixing; positive matrix factorization;

机译：高光谱图像;光谱分解;正矩阵分解;

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1. Unsupervised Nonlinear Hyperspectral Unmixing Based on Bilinear Mixture Models via Geometric Projection and Constrained Nonnegative Matrix Factorization [J] . Bin Yang, Bin Wang, Zongmin Wu Remote Sensing . 2018,第5期

机译：基于几何投影和约束非负矩阵分解的双线性混合模型的无监督非线性高光谱分解
2. Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery [J] . Yuntao Qian, Fengchao Xiong, Shan Zeng, Geoscience and Remote Sensing, IEEE Transactions on . 2017,第3期

机译：高光谱图像盲分解的矩阵矢量非负张量分解
3. An Unsupervised Binary and Multiple Change Detection Approach for Hyperspectral Imagery Based on Spectral Unmixing [J] . Jafarzadeh Hamid, Hasanlou Mahdi Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2019,第12期

机译：基于光谱解密的超光谱图像的无监督二元和多变化检测方法
4. The Impact of Initialization Procedures on Unsupervised Unmixing of Hyperspectral Imagery using the Constrained Positive Matrix Factorization [C] . Yahya M. Masalmah, Miguel Velez-Reyes Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII; Proceedings of SPIE-The International Society for Optical Engineering; vol.6565 . 2007

机译：约束正矩阵分解对初始化过程对高光谱图像无监督分解的影响
5. Unsupervised unmixing of hyperspectral imagery using the constrained positive matrix factorization. [D] . Masalmah, Yahya M. 2008

机译：使用约束正矩阵分解对高光谱图像进行无监督分解。
6. Spectral Unmixing of Hyperspectral Remote Sensing Imagery via Preserving the Intrinsic Structure Invariant [O] . Yang Shao, Jinhui Lan, Yuzhen Zhang, 2018

机译：通过保留固有结构不变性的高光谱遥感影像的光谱分解
7. Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery [O] . Qian Yuntao, Xiong Fengchao, Zeng Shan, 2017

机译：高光谱图像盲分解的矩阵矢量非负张量分解

Unsupervised Unmixing of Hyperspectral Imagery using the Positive Matrix Factorization

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