基于有约束CNMF的遥感高光谱多光谱图像融合

doi:10.3969/j.issn.0255-8297.2016.06.001

应用科学学报 ›› 2016, Vol. 34 ›› Issue (6): 651-660.doi: 10.3969/j.issn.0255-8297.2016.06.001

基于有约束CNMF的遥感高光谱多光谱图像融合

刘洋, 徐洪平, 易航, 施清平, 夏伟强, 康健

北京宇航系统工程研究所, 北京 100076

收稿日期:2016-05-10 修回日期:2016-06-20 出版日期:2016-11-30 发布日期:2016-11-30
通信作者: 徐洪平,研究员,研究方向:飞行器总体设计、信息融合等,E-mail:xuhp0501@sina.com E-mail:xuhp0501@sina.com

Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF

LIU Yang, XU Hong-ping, YI Hang, SHI Qing-ping, XIA Wei-qiang, KANG Jian

Beijing Institute of Aerospace System Engineering, Beijing 100076, China

Received:2016-05-10 Revised:2016-06-20 Online:2016-11-30 Published:2016-11-30

摘要/Abstract

摘要：

针对高光谱图像空间分辨率较低的问题，设计了一种基于光谱解混的高光谱、多光谱图像融合算法（VSC-CNMF）. 结合遥感图像的实际物理特性，在混合像元分解时加入端元单形体最小体积约束和丰度稀疏约束，通过光谱退化、空间退化和迭代解混，实现不同图像间端元和丰度的匹配，获得了具有高空间分辨率的融合图像. 仿真实验表明，VSC-CNMF可得到具有更高空间质量和光谱质量的融合图像.

关键词: 最小体积约束, 非负矩阵分解, 图像融合, 高光谱图像, 稀疏约束, 空间分辨率

Abstract:

Hyperspectral images generally have lowspatial resolution due to limitations of the imaging spectrometer. In this paper, VSC-CNMF is designed to produce a fused image from hyperspectral and multi-spectral images. An end-member smallest volume and abundance sparseness constrained NMF (VSC-CNMF) algorithm is proposed based on the physical characteristics of remote sensing images. We match the end-member and abundance of two types of images by spectral and spatial degradations, and get the fused image with high spatial and spectral resolution according to some un-mixing update rules. Simulation results show that fused images with higher spatial and spectral quality can be obtained with VSC-CNMF.

Key words: non-negative matrix factorization, hyperspectral image, sparseness constraint, spatial resolution, minimum volume constraint, image fusion

中图分类号:

TP751

刘洋, 徐洪平, 易航, 施清平, 夏伟强, 康健. 基于有约束CNMF的遥感高光谱多光谱图像融合[J]. 应用科学学报, 2016, 34(6): 651-660.

LIU Yang, XU Hong-ping, YI Hang, SHI Qing-ping, XIA Wei-qiang, KANG Jian. Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF[J]. Journal of Applied Sciences, 2016, 34(6): 651-660.

参考文献

[1] 贺霖,潘泉,邸韡,李远清. 高光谱图像目标检测研究进展[J]. 电子学报,2009,37(9): 2016-2024. He L, Pan Q, Di W, Li Y Q. Research advance on target detection for hyperspectral imagery [J]. Acta Electronica Sinica, 2009, 37(9): 2016-2024. (in Chinese)
[2] 王楠,张良培,杜博. 最小光谱相关约束NMF的高光谱遥感图像混合像元分解[J]. 武汉大学学报: 信息科学版,2014,39(1): 22-25. Wang N, Zhang L P, Du B. Minimum spectral correction constraint algorithm based on nonnegative matrix factorization for hyperspectral unmixing [J]. Geomatics and Information Science of Wuhan University, 2014, 39(1): 22-25. (in Chinese)
[3] Iordache M D, Bioucas-Dias J M, Plaza A. Sparse unmixing of hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2014-2039.
[4] Nascimento J M P, Bioucas-Dias J M. Vertex component analysis: a fast algorithm to unmix hyperspectral data [J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(4): 898-910.
[5] Boardman J, Kruse F, Green R, Mapping target signatures via partial unmixing of AVIRIS data [J]. Summaries of JPL Airborne Earth Science Workshop, 1995: 23-26.
[6] Winter M E. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data [C]//Proceedings of SPIE Image Spectrometry USA: SPIE, 1999: 266-277.
[7] Huang Q H, Wang S, Liu Z. Improved image feature extraction based on independent component analysis [J]. Opto-electronic, 2007, 34(1): 123-125.
[8] Lee D D, Seung H S. Learning the parts of objects by non-negative matrix factorization [J]. Nature, 1999, 401(6755): 788-791.
[9] Miao L, Qi H. 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.
[10] Li M, Qi H. End-member 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.
[11] Qian Y, Jia S. Hyperspectral unmixing via L1/2 sparsity constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 4282-4297.
[12] Dalla M M, Prasad S, Pacifici F, Gamba P, Chanussot J, Benediktsson J A. Challenges and opportunities of multimodality and data fusion in remote sensing [J]. Proceedings of the IEEE, 2015, 103(9): 1585-1601.
[13] Pradhan P S, King R L, Younan N H, Holcomb D W. Estimation of the number of decomposition levels for a wavelet-based multiresolution multisensory image fusion [J]. IEEE Transaction on Geoscience and Remote Sensing, 2006, 44(10): 3674-3686.
[14] Palsson F, Sveinsson J R, Ulfarsson M O, Benediktsson J A. Model-based fusion of multiand hyperspectral images using PCA and wavelets [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2652-2663.
[15] Guo Q, Wang Q, Ehlers M, Zhang H. Quality studies for N-band image fusion [C]// International Conference on Fuzzy Theory and Its Applications (iFUZZY 2015), Yilan, 2015: 6-11.
[16] Zhu X X, Grohnfeldt C, Bamler R. Exploiting joint sparsity for pansharpening: the jsparsefialgorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2664-2681.
[17] Yokoya N, Yairi T, Iwasaki A. Coupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion [J]. IEEE Transactions on Geoscience Remote Sensing, 2012, 50(2): 528-537.
[18] Bendoumi M A, He M, Mei S. Hyperspectral image resolution enhancement using highresolution multispectral image based on spectral unmixing [J]. IEEE Transactions on Geoscience Remote Sensing, 2014, 52(10): 6574-6583.
[19] ZhangY F. Spatial resolution enhancement of hyperspectral image based on the combination of spectral mixing model and observation model [C]//Proceedings of SPIE-The International Society for Optical Engineening, 2014: 924405.
[20] Nezhad Z H, Karami A, Heylen R. Scheunders P. Fusion of hyperspectral and multispectral images using spectral unmixing and sparse coding [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 99: 1-13.
[21] 魏一苇,黄世奇,王艺婷,卢云龙,刘代志. 基于体积和稀疏约束的高光谱混合像元分解算法[J]. 红外与激光工程,2014, 43(4): 1247-1254. Wei Y W, Huang S Q, Wang Y T, Lu Y L, Liu D Z. Volume and sparseness constrained algorithm for hyper-spectral un-mixing [J]. Infrared and Laser Engineering, 2014, 43(4): 1247-1254. (in Chinese)
[22] Hoyer P O. Non-negative matrix factorization with sparseness constraints [J]. The Journal of Machine Learning Research, 2004, 5: 1457-1469.
[23] Huang D,Swanson E A,Lin C P,Schuman J S, StinsonWG, Chang W, Hee M R, Flotte T, Gregory K, Puliafito C A. Optical coherence tomography [J]. Science, 1991, 254(5035): 1178-1181.

基于有约束CNMF的遥感高光谱多光谱图像融合

Hyperspectral and Multi-spectral Data Fusion Based on Constraint CNMF

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