基于混合像元空间与谱间相关性模型的NMF线性盲解混

doi:10.11947/j.AGCS.2019.20180054

测绘学报 ›› 2019, Vol. 48 ›› Issue (9): 1151-1160.doi: 10.11947/j.AGCS.2019.20180054

基于混合像元空间与谱间相关性模型的NMF线性盲解混

袁博

南阳理工学院计算机与信息工程学院, 河南南阳 473000

收稿日期:2018-03-06 修回日期:2019-06-07 出版日期:2019-09-20 发布日期:2019-09-25
作者简介:袁博(1982-),男,博士,讲师,研究方向为高光谱数据处理、物联网应用。
基金资助:
国家自然科学基金（41371353）

NMF linear blind unmixing method based on mixed pixel's spatial and spectral correlation model

YUAN Bo

School of Computer and Information Engineering, Nanyang Institute of Technology, Nanyang 473000, China

Received:2018-03-06 Revised:2019-06-07 Online:2019-09-20 Published:2019-09-25
Supported by:
The National Natural Science Foundation of China (No. 41371353)

摘要/Abstract

摘要： 基于相关性分析的高光谱解混算法，通常缺少对高光谱图像空间和光谱相关性特征的综合分析与利用，或对于先验知识的依赖程度较高。本文提出一种基于混合像元空间与谱间相关性模型的NMF线性盲解混算法。具体包括：通过改进马尔科夫随机场（MRF）模型，建立相邻像元间的空间相关模型；利用复杂度映射技术，建立相邻波段间的光谱相关模型；在NMF目标函数外部和内部分别引入上述两种模型，作为盲解混算法的约束条件。试验结果表明，该算法相对于区域相关的NMF解混算法（ACBNMF）、最小化光谱相关度约束的NMF方法（MSCCNMF）和最小体积约束的非负矩阵分解（MVCNMF）等代表性NMF解混参考算法，解混精度有所提高；同时，降低了对于先验知识的依赖程度，拓宽了适用范围。

Abstract: The present hyperspectral unmixing methods based on correlation analysis, either lack of comprehensive analysis and utilization of hyperspectral image's spatial & spectral correlation characteristics, or have a high dependence degree on prior knowledge. This paper proposes a NMF linear blind unmixing method based on mixed pixel's spatial and spectral correlation model. The method sets up spatial correlation model of adjacent pixels by improving Markov Random Filed(MRF) model, sets up spectral correlation model of adjacent bands by adopting complexity mapping technology, and introduces the two models respectively into NMF objective function externally and internally, as the constraints of the blind unmixing method. Experimental result indicates that, the proposed method can significantly reduced the degree of dependence on prior knowledge, comparing with other representative NMF reference methods including area-correlated spectral unmixing method based on Bayesian nonnegative matrix factorization(ACBNMF), minimum spectral correlation constraint NMF(MSCCNMF) and minimum volume constrained nonnegative matrix factorization(MVCNMF), the unmixing accuracy is also improved.

Key words: nonnegative matrix factorization, spatial correlation, spectral correlation, Markov random field, complexity mapping

中图分类号:

P237

袁博. 基于混合像元空间与谱间相关性模型的NMF线性盲解混[J]. 测绘学报, 2019, 48(9): 1151-1160.

YUAN Bo. NMF linear blind unmixing method based on mixed pixel's spatial and spectral correlation model[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(9): 1151-1160.

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基于混合像元空间与谱间相关性模型的NMF线性盲解混

NMF linear blind unmixing method based on mixed pixel's spatial and spectral correlation model

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