| 改进的高光谱解混初始化方法及其适用性分析 |
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| 引用本文： | 魏磊,丁来中,王文杰,高彦涛,黄鹏飞,李春意,张永杰.改进的高光谱解混初始化方法及其适用性分析[J].测绘通报,2022,0(8):61-67. |
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| 作者姓名： | 魏磊 丁来中 王文杰 高彦涛 黄鹏飞 李春意 张永杰 |
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| 作者单位： | 1. 河南省地质矿产勘查开发局测绘地理信息院, 河南 郑州 450006;2. 河南理工大学测绘与国土信息工程学院, 河南 焦作 454000 |
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| 基金项目： | 国家自然科学基金(U1810203;41671507);河南省科技攻关项目(212102310404);河南省青年骨干教师项目(2019GGJS059) |
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| 摘 要： | 高光谱影像中存在大量的混合像元,极大地限制了高光谱影像的定量应用,高效且精准地进行像元解混尤为重要。端元矩阵的初始化、算法本身的代价函数及其迭代规则,三者的不同往往会导致获取的最终端元光谱和端元丰度的不同。在不同条件下,选取适当的初始化方法、代价函数和迭代规则,使得高光谱解混结果更优尤为重要。本文改进了一种基于欧氏距离和光谱信息散度的分块初始化方法(IBISS),改进后方法在中低信噪比情况下优于其他初始化方法。同时针对初始化、算法本身这两个方面进行大量试验,结果表明:①分块初始化优于全局初始化;②梯度迭代NMF算法相比于乘性迭代NMF算法,具有更快的收敛速度,但容易陷入局部最小值;③乘性迭代分块NMF算法相比于乘性迭代标准NMF算法能够获取更好的端元丰度信息;④梯度迭代分块NMF算法不适用于随机初始化后的光谱解混过程。
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| 关 键 词： | 高光谱影像 非负矩阵分解 光谱解混 初始化 |
| 收稿时间： | 2021-10-20 |
Improved hyperspectral unmixing initialization method and its applicability analysis |
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| WEI Lei,DING Laizhong,WANG Wenjie,GAO Yantao,HUANG Pengfei,LI Chunyi,ZHANG Yongjie.Improved hyperspectral unmixing initialization method and its applicability analysis[J].Bulletin of Surveying and Mapping,2022,0(8):61-67. |
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| Authors: | WEI Lei DING Laizhong WANG Wenjie GAO Yantao HUANG Pengfei LI Chunyi ZHANG Yongjie |
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| Institution: | 1. Institute of Surveying, Mapping and Geoinformation of Henan Provincial Bureau of Geo-exploration and Mineral Development, Zhengzhou 450006, China;2. School of Surveying and Landing Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China |
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| Abstract: | There are a large number of mixed pixels in hyperspectral images, which greatly limit the quantitative application of hyperspectral images, and it is especially important to perform pixel unmixing efficiently and accurately. The initialization of the endmember matrix, the cost function of the algorithm itself and the iterative rules of the algorithm often result in different end-element spectra and end-batch abundances. Under different conditions, it is especially important to select appropriate initialization methods, cost functions and iterative rules to make the hyperspectral unmixing results better. In this paper, a new block initialization method based on Euclidean distance and spectral information divergence is improved. The improved block initialization method is superior to other initialization methods in the case of low to medium SNR. At the same time, a lot of experiments are carried out on the two aspects of initialization and algorithm itself. The results show that:①The block initialization is better than the global initialization.②The gradient iterative NMF algorithm is faster than the multiplicative iterative NMF algorithm, but easy to fall into local minimum.③Block multiplicative iterative NMF algorithm can obtain better endmember abundance information than standard multiplicative iterative NMF algorithm;④Block gradient iterative NMF algorithm is not applicable to the spectral unmixing process after random initialization. |
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| Keywords: | hyperspectral image non-negative matrix factorization spectral unmixing initialization |
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