激光与光电子学进展, 2019, 56 (16): 161001, 网络出版: 2019-08-05   

基于约束非负矩阵分解的高光谱图像解混 下载: 997次

Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization
方帅 1,**王金明 1,*曹风云 2,3
作者单位
1 合肥工业大学计算机与信息学院人工智能与数据挖掘研究室, 安徽 合肥 230601
2 合肥工业大学工业安全与应急技术安徽省重点实验室, 安徽 合肥 230601
3 合肥师范学院计算机学院, 安徽 合肥 230601
引用该论文

方帅, 王金明, 曹风云. 基于约束非负矩阵分解的高光谱图像解混[J]. 激光与光电子学进展, 2019, 56(16): 161001.

Shuai Fang, Jinming Wang, Fengyun Cao. Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161001.

参考文献

[1] Goetz A F H, Rowan L C. Geologic remote sensing[J]. Science, 1981, 211(4484): 781-791.

[2] Goetz A F H, Vane G, Solomon J E, et al. . Imaging spectrometry for earth remote sensing[J]. Science, 1985, 228(4704): 1147-1153.

[3] Plaza A, Benediktsson J A, Boardman J W, et al. Recent advances in techniques for hyperspectral image processing[J]. Remote Sensing of Environment, 2009, 113(S1): S110-S122.

[4] 蔡亮红, 丁建丽. 基于高光谱多尺度分解的土壤含水量反演[J]. 激光与光电子学进展, 2018, 55(1): 013001.

    Cai L H, Ding J L. Inversion of soil moisture content based on hyperspectral multi-scale decomposition[J]. Laser & Optoelectronics Progress, 2018, 55(1): 013001.

[5] LillesandT, Kiefer RW, ChipmanJ. Remote sensing and image interpretation[M]. Hoboken: John Wiley & Sons, Inc., 2014.

[6] 于纯妍, 赵猛, 宋梅萍, 等. 基于目标约束与谱空迭代的高光谱图像分类方法[J]. 光学学报, 2018, 38(6): 0628003.

    Yu C Y, Zhao M, Song M P, et al. Hyperspectral image classification method based on targets constraint and spectral-spatial iteration[J]. Acta Optica Sinica, 2018, 38(6): 0628003.

[7] 李非燕, 霍宏涛, 白杰, 等. 基于稀疏表示和自适应模型的高光谱目标检测[J]. 光学学报, 2018, 38(12): 1228004.

    Li F Y, Huo H T, Bai J, et al. Hyperspectral target detection based on sparse representation and adaptive model[J]. Acta Optica Sinica, 2018, 38(12): 1228004.

[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] PlazaA, MartínG, PlazaJ, et al. Recent developments in endmember extraction and spectral unmixing[M] ∥Prasad S, Bruce L, Chanussot J. Optical remote sensing. Augmented vision and reality. Berlin, Heidelberg: Springer, 2011, 3: 235- 267.

[10] Boardman J W. Mapping target signatures via partial unmixing of aviris data[C]∥Fifth Annual JPL Airborne Geoscience Workshop. [S.l.]: JPL Publication, 1995: 23- 26.

[11] Boardman J W. Automating spectral unmixing of AVIRIS data using convex geometry concepts[C]∥4th Annual JPL Airborne Geoscience Workshop. [S.l.]: JPL Publication, 1993: 11-14: 23- 26.

[12] Winter M E. N-FINDR:an algorithm for fast autonomous spectral end-member determination in hyperspectral data[J]. Proceedings of SPIE, 1999, 3753: 266-275.

[13] Luo W F, Zhang B, Jia X P. New improvements in parallel implementation of N-FINDR algorithm[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(10): 3648-3659.

[14] Neville RA, StaenzK, SzerediT, et al. Automatic endmember extraction from hyperspectral data for mineral exploration[C]∥Fourth International Airborne Remote Sensing Conference and Exhibition / 21st Canadian Symposium on Remote Sensing, June 21-24, 1999, Ottawa, Ontario, Canada. Canada: Earth Sciences Sector, 1999.

[15] Gruninger J H, Ratkowski A J, Hoke M L. The sequential maximum angle convex cone (SMACC) endmember model[J]. Proceedings of SPIE, 2004, 5425: 1-14.

[16] 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.

[17] Fuhrmann D R. Simplex shrink-wrap algorithm[J]. Proceedings of SPIE, 1999, 3718: 501-511.

[18] 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.

[19] 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.

[20] 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.

[21] 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): 093004.

    Yan Y, Hua W S, Cui Z H, et al. Classification and volume for hyperspectral endmember extraction[J]. Laser & Optoelectronics Progress, 2018, 55(9): 093004.

[22] Tu T M. Unsupervised signature extraction and separation in hyperspectral images: a noise-adjusted fast independent component analysis[J]. Optical Engineering, 2000, 39(4): 897-906.

[23] 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.

[24] Dobigeon N, Moussaoui S, Tourneret J Y, et al. Bayesian separation of spectral sources under non-negativity and full additivity constraints[J]. Signal Processing, 2009, 89(12): 2657-2669.

[25] Plaza A, Martinez P, Pérez R, et al. Spatial/spectral endmember extraction by multidimensional morphological operations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(9): 2025-2041.

[26] Rogge D M, Rivard B, Zhang J, et al. Integration of spatial-spectral information for the improved extraction of endmembers[J]. Remote Sensing of Environment, 2007, 110(3): 287-303.

[27] Martin G, Plaza A. Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5(2): 380-395.

[28] Gao L R, Gao J W, Li J, et al. Multiple algorithm integration based on ant colony optimization for endmember extraction from hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 2569-2582.

[29] Gao L R, Zhuang L N, Wu Y F, et al. A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm[J]. Soft Computing, 2016, 20(12): 4669-4683.

[30] Zhao H H, Jiang Y M, Wang T, et al. A method based on the adaptive cuckoo search algorithm for endmember extraction from hyperspectral remote sensing images[J]. Remote Sensing Letters, 2016, 7(3): 289-297.

[31] ZareA, GaderP. Piece-wise convex spatial-spectral unmixing of hyperspectral imagery using possibilistic and fuzzy clustering[C]∥2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011), June 27-30, 2011, Taipei, Taiwan. New York: IEEE, 2011: 741- 746.

[32] Filippi A M, Archibald R. Support vector machine-based endmember extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(3): 771-791.

[33] Feng X R, Li H C, Li J, et al. Hyperspectral unmixing using sparsity-constrained deep nonnegative matrix factorization with total variation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 6245-6257.

[34] Cai D, He X F, Han J W, et al. Graph regularized nonnegative matrix factorization for data representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1548-1560.

[35] Iordache M D. Bioucas-Dias J M, Plaza A. Total variation spatial regularization for sparse hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(11): 4484-4502.

[36] BelkinM, NiyogiP. Laplacian eigenmaps and spectral techniques for embedding and clustering[C]∥Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, December 3-8, 2001, Vancouver, British Columbia, Canada. Cambridge: MIT Press, 2002: 585- 591.

[37] Chung F RK, Graham FC. Spectral graph theory[M]. Providence, Rhode Island: American Mathematical Society, 1997.

[38] Plaza J. Hendrix E M T, García I, et al. On endmember identification in hyperspectral images without pure pixels: a comparison of algorithms[J]. Journal of Mathematical Imaging and Vision, 2012, 42(2/3): 163-175.

[39] Zhu F Y, Wang Y, Fan B, et al. Spectral unmixing via data-guided sparsity[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5412-5427.

[40] Li J. Bioucas-Dias J M, Plaza A, et al. Robust collaborative nonnegative matrix factorization for hyperspectral unmixing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6076-6090.

方帅, 王金明, 曹风云. 基于约束非负矩阵分解的高光谱图像解混[J]. 激光与光电子学进展, 2019, 56(16): 161001. Shuai Fang, Jinming Wang, Fengyun Cao. Hyperspectral Image Unmixing Based on Constrained Nonnegative Matrix Factorization[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161001.

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