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

doi:10.11947/j.AGCS.2019.20180054

Abstract

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

CLC Number:

P237

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.

References

[1] 王毓乾. 基于空间-光谱分析的高光谱遥感影像稀疏解混研究[J]. 测绘学报, 2017, 46(8):1072. DOI:10.11947/j.AGCS.2017.20170167. WANG Yuqian. Hyperspectral imagery sparse unmixing based on spatial and spectral analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(8):1072. DOI:10.11947/j.AGCS.2017.20170167.
[2] 张良培, 武辰. 多时相遥感影像变化检测的现状与展望[J]. 测绘学报, 2017, 46(10):1447-1459. DOI:10.11947/j.AGCS.2017.20170340. ZHANG Liangpei, WU Chen. Advance and future development of change detection for multi-temporal remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1447-1459. DOI:10.11947/j.AGCS.2017.20170340.
[3] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755):788-791.
[4] XU Yifei, DENG Shuiguang, LI Xiaoli, et al. A sparse unmixing model based on NMF and its application in Raman image[J]. Neurocomputing, 2016(207):120-130.
[5] KAROUI M S, DEVILLE Y, HOSSEINI S, et al. Blind spatial unmixing of multispectral images:new methods combining sparse component analysis, clustering and non-negativity constraints[J]. Pattern Recognition, 2012, 45(12):4263-4278.
[6] MUSHTAQ Q, HAQ I U, AHMAD M, et al. Hyperspectral blind unmixing and multiple target detection using linear mixture model[J]. Advanced Materials Research, 2012(488-489):1224-1228.
[7] 施蓓琦, 刘春, 孙伟伟, 等. 应用稀疏非负矩阵分解聚类实现高光谱影像波段的优化选择[J]. 测绘学报, 2013, 42(3):351-358, 366. SHI Beiqi, LIU Chun, SUN Weiwei, et al. Sparse nonnegative matrix factorization for hyperspectral optimal band selection[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3):351-358, 366.
[8] 张盈, 张景雄. 顾及空间相关性的遥感影像信息量的度量方法[J]. 测绘学报, 2015, 44(10):1117-1124. DOI:10.11947/j.AGCS.2015.20140417. ZHANG Ying, ZHANG Jingxiong. Measure of information content of remotely sensed images accounting for spatial correlation[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(10):1117-1124. DOI:10.11947/j.AGCS.2015.20140417.
[9] 孔繁锵, 卞陈鼎, 李云松, 等. 非凸稀疏低秩约束的高光谱解混方法[J]. 西安电子科技大学学报(自然科学版), 2016, 43(6):116-121. KONG Fanqiang, BIAN Chending, LI Yunsong, et al. Hyperspectral unmixing method based on the non-convex sparse and low-rank constraints[J]. Journal of Xidian University, 2016, 43(6):116-121.
[10] JIA Sen, QIAN Yuntao. Constrained nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(l):161-173.
[11] MIAO Lidan, QI Hairong. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 15(8):765-777.
[12] 詹锡兰, 吴波. 一种基于高斯马尔可夫随机场模型的混合像元分解方法[J]. 福州大学学报(自然科学版), 2011, 39(1):60-66. ZHAN Xilan, WU Bo. A method of spectral mixture analysis based on Gaussian Markov random field model[J]. Journal of Fuzhou University (Natural Science Edition), 2011, 39(1):60-66.
[13] 刘建军, 吴泽彬, 韦志辉, 等. 基于空间相关性约束稀疏表示的高光谱图像分类[J]. 电子与信息学报, 2012, 34(11):2666-2671. LIU Jianjun, WU Zebin, WEI Zhihui, et al. Spatial correlation constrained sparse representation for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2012, 34(11):2666-2671.
[14] CHEN Xiawei, YU Jing, SUN Weidong. Area-correlated spectral unmixing based on Bayesian nonnegative matrix factorization[J]. Open Journal of Applied Sciences, 2013, 3(1):41-46.
[15] 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):22-26. WANG Nan, ZHANG Liangpei, DU Bo. Minimum spectral correlation constraint algorithm based on non-negative matrix factorization for hyperspectral unmixing[J]. Geomatics and Information Science of Wuhan University, 2014, 39(1):22-26.
[16] 许宁, 尤红建, 耿修瑞, 等. 基于光谱相似度量的高光谱图像多任务联合稀疏光谱解混方法[J]. 电子与信息学报, 2016, 38(11):2701-2708. XU Ning, YOU Hongjian, GENG Xiurui, et al. Multi-task jointly sparse spectral unmixing method based on spectral similarity measure of hyperspectral imagery[J]. Journal of Electronics & Information Technology, 2016, 38(11):2701-2708.
[17] CHEN Bolin, LI Min, WANG Jianxin, et al. Disease gene identification by using graph kernels and Markov random fields[J]. Science China Life Sciences, 2014, 57(11):1054-1063.
[18] 尤传雨, 刘文波, 常军. 基于复杂追踪理论的结构模态参数识别[J]. 苏州科技学院学报(工程技术版), 2016, 29(2):38-42, 50. YOU Chuanyu, LIU Wenbo, CHANG Jun. Structural model parameter identification based on complexity pursuit[J]. Journal of Suzhou University of Science and Technology (Engineering and Technology), 2016, 29(2):38-42, 50
[19] HOYER P O. Non-negative matrix factorization with sparseness constraints[J]. The Journal of Machine Learning Research, 2004(5):1457-1469.
[20] YIN Gang, BUI T. Stochastic systems arising from Markov modulated empirical measures[J]. Journal of Systems Science and Complexity, 2017, 30(5):999-1011.
[21] DENG Huawu, CLAUSI D A. Unsupervised image segmentation using a simple MRF model with a new implementation scheme[J]. Pattern Recognition, 2004, 37(12):2323-2335.
[22] 亓琳, 史泽林. 一种基于DA-GMRF的无监督图像分割方法[J]. 光电工程, 2007, 34(10):88-92. QI Lin, SHI Zelin. Unsupervised image segmentation based on DA-GMRF[J]. Opto-Electronic Engineering, 2007, 34(10):88-92.
[23] BIOUCAS-DIAS J M, NASCIMENTO J M P. Hyperspectral subspace identification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(8):2435-2445.
[24] GENG Xiurui, JI Luyan, SUN Kang. Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(5):403-412.
[25] 吴金彪. D-N交替迭代法及其收敛性分析[J]. 数值计算与计算机应用, 2002, 23(2):121-130. WU Jinbiao. D-N commutative method and its convergence[J]. Journal on Numerical Methods and Computer Applications, 2002, 23(2):121-130.