基于约束误差不变的高光谱图像端元优化模型

doi:10.7523/j.ucas.2020.0008

中国科学院大学学报 ›› 2022, Vol. 39 ›› Issue (1): 83-90.DOI: 10.7523/j.ucas.2020.0008

• 电子信息与计算机科学 • 上一篇

基于约束误差不变的高光谱图像端元优化模型

王为家, 耿修瑞

中国科学院空天信息创新研究院中国科学院空间信息处理与应用系统技术重点实验室, 北京 100094;中国科学院大学, 北京 100049

收稿日期:2020-01-13 修回日期:2020-03-26 发布日期:2021-05-31
通讯作者: 王为家
基金资助:
国家自然科学基金（61805246）资助

Endmember optimization model of hyperspectral image based on constant constraint error

WANG Weijia, GENG Xiurui

Key Laboratory of Technology in Geo-spatial Information Processing and Application System of CAS, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China;University of Chinese Academy of Sciences, Beijing 100049, China

Received:2020-01-13 Revised:2020-03-26 Published:2021-05-31

摘要/Abstract

摘要： 线性混合模型（linear mixture model，LMM）在端元提取中起着至关重要的作用。在LMM假设下，理想的端元提取效果需要重建误差最小。但是在实际的高光谱数据集中噪声是不可避免的，单纯追求重建误差最小化可能会导致最终结果偏离真实端元。为平衡重建误差与噪声的影响，使用几何优化模型计算重建误差，通过约束重建误差来最小化单形体体积，提出新的误差约束优化模型EIC-OSV （error invariant constrained-optimal simplex volume）。模拟数据及真实数据下的实验表明，EIC-OSV可以提高现有端元提取方法的准确性。

关键词: 高光谱, 端元提取, 单形体, 优化

Abstract: The linear mixture model (LMM) plays a crucial role in endmember extraction. In general, under the assumption of LMM, the endmembers in an image can be obtained by minimizing the model reconstruction error. However, due to the existence of noise, the endmembers corresponding to the minimum model reconstruction error often deviate from the real ones. In order to balance the effects of reconstruction error and noise, the geometric optimization model is adopted to evaluate the reconstruction error in this paper and the reconstruction error is used as the constraint to further minimize the volume of the simplex. The presented method is called the error invariant constrained-optimal simplex volume method (EIC-OSV). The experiments with simulated and real hyperspectral data demonstrate that EIC-OSV can improve the overall accuracy of the popular endmember extraction methods.

Key words: hyperspectral, endmember extraction, simplex, optimization

中图分类号:

TP751

王为家, 耿修瑞. 基于约束误差不变的高光谱图像端元优化模型[J]. 中国科学院大学学报, 2022, 39(1): 83-90.

WANG Weijia, GENG Xiurui. Endmember optimization model of hyperspectral image based on constant constraint error[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(1): 83-90.

参考文献

[1] 李二森, 朱述龙, 周晓明, 等. 高光谱图像端元提取算法研究进展与比较[J]. 遥感学报, 2011, 15(4):659-679.
[2] Boardman J W. Automating spectral unmixing of AVIRIS data using convex geometry concepts[J]. Summaries of the Fourth Annual JPL Airborne Geoscience Workshop, JPL Publication, 1993, 1:11-14.
[3] Winter M E. N-FINDR:an algorithm for fast autonomous spectral end-member determination in hyperspectral data[C]//International Symposium on Optics Science, Engineering, and Instrumentation. Proc SPIE 3753, Imaging Spectrometry V, Denver, CO, USA. 1999, 3753:266-275.
[4] 杨可明, 魏华锋, 刘飞, 等. 以光谱信息熵改进的N-FINDR高光谱端元提取算法[J]. 地球信息科学学报, 2015, 17(8):979-985.
[5] Chang C I, Wu C C, Liu W, et al. A new growing method for simplex-based endmember extraction algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(10):2804-2819.
[6] 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.
[7] Geng X R, Xiao Z Q, Ji L Y, et al. A Gaussian elimination based fast endmember extraction algorithm for hyperspectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79:211-218.
[8] Sun K, Geng X R, Wang P S, et al. A fast endmember extraction algorithm based on gram determinant[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6):1124-1128.
[9] Wang X Y, Zhong Y F, Xu Y, et al. Saliency-based endmember detection for hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(7):3667-3680.
[10] 严阳, 华文深, 崔子浩, 等. 高光谱分类体积的端元提取[J]. 激光与光电子学进展, 2018, 55(9):446-451.
[11] 杨随心, 耿修瑞, 杨炜暾, 等. 一种基于谱聚类算法的高光谱遥感图像分类方法[J]. 中国科学院大学学报, 2019, 36(2):267-274.
[12] Craig M D. Minimum-volume transforms for remotely sensed data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32(3):542-552.
[13] Berman M, Kiiveri H, Lagerstrom R, et al. ICE:a statistical approach to identifying endmembers in hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(10):2085-2095.
[14] Zare A, Gader P. Sparsity promoting iterated constrained endmember detection in hyperspectral imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(3):446-450.
[15] Miao L D, Qi H R. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3):765-777.
[16] Hendrix E M T, Garcia I, Plaza J, et al. A new minimum-volume enclosing algorithm for endmember identification and abundance estimation in hyperspectral data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(7):2744-2757.
[17] Chan T H, Chi C Y, Huang Y M, et al. A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing[J]. IEEE Transactions on Signal Processing, 2009, 57(11):4418-4432.
[18] Geng X R, Ji L Y, Zhao Y C, et al. A new endmember generation algorithm based on a geometric optimization model for hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(4):811-815.
[19] Chang C I, Du Q. Estimation of number of spectrally distinct signal sources in hyperspectral imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(3):608-619.
[20] Swayze G, Clark R N, Kruse F, et al. Ground-truthing AVIRIS mineral mapping at Cuprite, Nevada[C]//Summaries of the Third Annual JPL Airborne Geoscience Workshop. Denver, CO:JPL Publication, 1992:47-49.

基于约束误差不变的高光谱图像端元优化模型

Endmember optimization model of hyperspectral image based on constant constraint error

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