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基于去噪降维和蝙蝠优化的高光谱图像盲解混算法

Blind Separation Algorithm for Hyperspectral Image Based on the Denoising Reduction and the Bat Optimization

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摘要

为解决盲源分离技术难以直接用于高光谱图像解混这一问题, 将丰度非负及和为1约束作为盲源分离的目标函数, 改变传统的独立性假设;同时, 针对目标函数中具有大量的局部极小, 引入蝙蝠优化算法, 解决传统梯度类优化算法易陷入局部极值的问题.在降维过程中, 提出一种基于奇异值分解去噪的正交子空间投影的降维方法.仿真数据和真实遥感数据实验表明, 所提出算法收敛速度和解混准确度高, 具有较强的抗噪声干扰能力, 适用于像元纯度很低的高光谱图像解混.

Abstract

In order to solve the problem that the blind source separation is difficult to be directly applied to the hyperspectral unmixing, the linear spectral mixture model was introduced in the presence of Abundance Non-negative Constraint (ANC) and Abundance Sum-to-one Constraint (ASC) as the objective function of the blind source separation to change the traditional independence assumption。 Then, the Bat Algorithm (BA) was introduced to optimize the objective function。 This algorithm solves the problem that the traditional gradient optimization algorithm is easy to fall into the local extremum。 A method was proposed for dimensionality reduction, which is based on Singular Value Decomposition Denoising-orthogonal Subspace Projection (SVDD-OSP)。 The experimental results on synthetic data and real remote sensing data indicate that the proposed algorithm has a high convergence rate and a high accuracy。 In addition, it has the strong anti noise interference ability and can be applied to the data with a low purity。

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中图分类号:TP751

DOI:

基金项目:国家自然科学基金(No.61401307)、中国博士后科学基金(No.2014M561184)和天津市应用基础与前沿技术研究计划(No.15JCYBJC17100)资助

收稿日期:2015-12-07

修改稿日期:2016-02-18

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作者单位    点击查看

贾志成:河北工业大学 电子信息工程学院, 天津 300401
薛允艳:河北工业大学 电子信息工程学院, 天津 300401
陈雷:天津大学 精密仪器与光电子工程学院, 天津 30007天津商业大学 信息工程学院, 天津 300134
郭艳菊:河北工业大学 电子信息工程学院, 天津 300401
许浩达:河北工业大学 电子信息工程学院, 天津 300401

联系人作者:贾志成(jiazc@hebut.edu.cn)

备注:贾志成(1957-), 男, 教授, 主要研究方向为高光谱图像处理。

【1】TONG Q, XUE Y, ZHANG L. Progress in hyperspectral remote sensing science and technology in China over the past three decades[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 70-91.

【2】MA W K, BIOUCAS-D J M, CHAN T H, et al. A signal processing perspective on hyperspectral unmixing: Insights from remote sensing[J]. IEEE Signal Processing Magazine, 2014, 31(1): 67-81.

【3】CHAN T H, MA W K, AMBIKAPATHI A M, et al. A simplex volume maximization framework for hyperspectral endmember extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(11): 4177-4193.

【4】NASCIMENTO J M P, DIAS J M B. Vertex component analysis: A fast algorithm to unmix hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.

【5】LI J, BIOUCAS-DIAS J M。 Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data[J]。 IEEE Geoscience and Remote Sensing Symposium, 2008, 3(3): 250-253。

【6】CALLICO G M, LOPEZ S, AGUILAR B, et al. Parallel implementation of the modified vertex component analysis algorithm for hyper-spectral unmixing using openCL[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(8): 3650-3659.

【7】LI J, AGATHOS A, ZAHARIE D, et al. Minimum volume simplex analysis: a fast algorithm for linear hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(9): 5067-5082.

【8】BIOUCAS-DIAS J M, PLAZA A, DOBIGEON N, et al。 Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches[J]。 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 354-379。

【9】COMON P, JUTTEN C。 Handbook of blind source separation: independent component analysis and applications[M]。 Academic press, 2010, 4(2): 179-420。

【10】NASCIMENTO J M P, DIAS J M B. Does independent component analysis play a role in unmixing hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(1): 175-187.

【11】YANG X S。 A new metaheuristic bat-inspired algorithm[M]。Nature inspired cooperative strategies for optimization。 Springer Berlin Heidelberg, 2010: 65-74。

【12】JOLLIFFE I。 Principal component analysis[M]。 John Wiley & Sons, Ltd, 2002。

【13】ACITO N, DIANI M, CORSINI G. Hyperspectral signal subspace identification in the presence of rare vectors and signal-dependent noise[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 283-299.

【14】SONG M, CHANG C I。 A theory of recursive orthogonal subspace projection for hyperspectral imaging[J]。 IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(6): 3055-3072。

【15】WANG N, DU B, ZHANG L, et al。 An abundance characteristic based independent component analysis for hyperspectral unmixing[J]。 IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 416-428。

【16】XIA W, LIU X, WANG B, et al. Independent component analysis for blind unmixing of hyperspectral imagery with additional constraints[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2165-2179.

【17】EBERHART R C, KENNEDY J。 A new optimizer using particle swarm theory[C]。 Proceedings of the sixth international symposium on micro machine and human science。 1995, 1: 39-43。

【18】YANG X S. Harmony search as a metaheuristic algorithm[M]. Music-inspired harmony search algorithm. Springer Berlin Heidel -berg, 2009: 1-14.

【19】TAN He-ren, XIE Sheng-li. Blind separation algorithm based on QR decomposition and penalty function[J]. Computer Engineering, 2003, 29(17): 55-57.
覃和仁, 谢胜利. 基于QR分解与罚函数方法的盲分离算法[J]. 计算机工程, 2003, 29(17): 55-57.

【20】NASCIMENTO J M P, BIOUCAS-DIAS J M。 Hyperspectral unmixing based on mixtures of dirichlet components[J]。 IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 863-878。

【21】MIAO L, QI H. Endmember extraction from highly mixed data using minimum volume constrained non negative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765-777.

【22】HEINZ D C, CHANG C I. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(3): 529-545.

【23】http: //speclab.cr.usgs.gov/spectral-lib.html.

【24】http: //cobweb.ecn.purdue.edu/biehl/Multi-Spec.
Foundation item: The National Natural Science Foundation of China (No.61401307), the China Postdoctoral Science Foundation of China (No.2014M561184) and the Tianjin Application Infrastructure and Frontier Technology Research Projects(No.15JCYBJC17100)

引用该论文

JIA Zhi-cheng,XUE Yun-yan,CHEN Lei,GUO Yan-jun,XU Hao-da. Blind Separation Algorithm for Hyperspectral Image Based on the Denoising Reduction and the Bat Optimization[J]. ACTA PHOTONICA SINICA, 2016, 45(5): 0511001

贾志成,薛允艳,陈雷,郭艳菊,许浩达. 基于去噪降维和蝙蝠优化的高光谱图像盲解混算法[J]. 光子学报, 2016, 45(5): 0511001

被引情况

【1】介邓飞,李泽海,赵竣威,连裕翔,魏 萱. 基于高光谱漫透射成像可视化检测脐橙可溶性固形物. 发光学报, 2017, 38(5): 685-691

【2】曾海金,蒋家伟,赵佳佳,王艺卓,谢晓振. L1-2空谱全变差正则化下的高光谱图像去噪. 光子学报, 2019, 48(10): 1010002--1

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