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Published： 2017-3 ，
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Ruihao ZHANG, Wenfei LUO, Liang ZHONG, et al. Double-population differential evolution algorithm for bilinear spectral unmixing based on FAN model. [J]. Journal of Remote sensing 21(2):239-252(2017)
双线性混合模型是近年来非线性光谱解混的研究重点之一，其克服了线性混合模型无法描述地物多重散射作用的缺陷，能够更精确地还原真实的地物光谱混合过程。然而，限于模型的复杂性，目前在缺乏准确的端元先验知识的条件下进行双线性光谱解混仍是一项具有挑战性的任务。差分进化算法(DE)是一种具有良好全局搜索能力的群智能优化算法，其优化求解过程无需进行复杂的数学推导，为双线性光谱解混问题提供了一种有效的解决途径。为此，本文以FAN双线性混合模型为例，提出了一种双种群机制的差分进化算法(记为DE-FAN)，实现非监督双线性光谱解混。DE-FAN算法通过建立端元与丰度两个种群的交替进化机制寻找最优解，同时在迭代中引入自适应重构策略增强种群多样性，降低算法陷入局部最优解的风险，最终实现端元与丰度的同时估计。通过模拟图像及真实图像的解混实验进行算法检验，证明DE-FAN算法较之传统非线性解混算法具有更高的解混精度及解混效率。
Hyperspectral unmixing plays an important role in remote sensing applications by extracting the pure constituent spectra and correspondent fractions in mixing pixels. Bilinear mixing models have recently become a hot topic in nonlinear spectral unmixing research. Nevertheless
most of the existing bilinear unmixing algorithms require prior knowledge about the endmembers
which can be regarded as supervised algorithms. This paper reports on a new unsupervised unmixing algorithm based on the FAN bilinear mixing model by employing the differential evolution (DE) algorithm (i.e.
DE-FAN)
which can solve the bilinear unmixing problems more efficiently and accurately compare with existing unsupervised bilinear unmixing algorithms.Considering the bilinear unmixing problem with hyperspectral imagery
the proposed algorithm makes the following improvements on the standard DE algorithm. First
the mutation factor is tuned dynamically rather than fixed during the iterative process
which can significantly enhance the neighborhood search capability. Second
considering the acceleration of the convergence rate
DE-FAN constructs a double-population (i.e.
endmember population and abundance population) framework such that an alternative evolution procedure is presented. Third
the population re-initialization tactic is introduced to enhance the capability of large-scale optimization. It can decrease the number of required population individuals
thus improving the computational efficiency. It can also significantly lower the trapping risk in local optimums. Finally
a cooperative co-evolution tactic is considered. The hyperspectral image is divided into several sub-images and then processed by the DE-FAN algorithm separately. The final solution of the endmember estimation can be obtained by averaging the best solutions from the sub-images. The remaining iterations can continue by fixing the endmember variables with the final endmember solution. The final solution of abundance estimation is achieved when the stopping criteria is met.We validate the DE-FAN algorithm by adopting synthetic datasets and real airborne visible/infrared imaging spectrometer images. Different endmember numbers
signal-to-noise ratios
and maximum abundances are also considered. Several state-of-the-art algorithms (i.e.
MVC-NMF
SISAL+FCLS
MU-LQM
N-FINDR+CNLS
geodesic simplex volume maximization
and PG-FAN) are compared. Experimental results conducted utilizing both synthetic and real datasets indicate that the proposed DE-FAN algorithm outperforms other algorithms by obtaining more accurate endmembers and abundance.The DE-FAN algorithm overcomes the LMM shortcomings and solves the bilinear unmixing problem. The unmixing experiments demonstrate that DE-FAN can obtain more accurate results than other classical unmixing algorithms. We will consider the influence of the penalty coefficient and attempt an adaptive method in future work. We will also extend our proposed DE-FAN to more bilinear models such as the polynomial post-nonlinear model and generalized bilinear model.
高光谱遥感光谱解混双线性混合模型差分进化算法双种群机制
hyperspectral remote sensingspectral unmixingbilinear mixing modeldifferential evolutiondouble-population algorithm
Altmann Y, Dobigeon N and Tourneret J Y. 2014. Unsupervised post-nonlinear unmixing of hyperspectral images using a Hamiltonian Monte Carlo algorithm. IEEE Transactions on Image Processing, 23(6): 2663–2675
Altmann Y, Halimi A, Dobigeon N and Tourneret J Y. 2012. Supervised nonlinear spectral unmixing using a postnonlinear mixing model for hyperspectral imagery. IEEE Transactions on Image Processing, 21(6): 3017–3025
Bioucas-Dias J M. 2009. A variable splitting augmented Lagrangian approach to linear spectral unmixing // Proceedings of the 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Grenoble: IEEE: 1–4
Bro R and De Jong S. 1997. A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11(5): 393–401
Chakravortty S, Shah E and Chowdhury A S. 2014. Application of spectral unmixing algorithm on hyperspectral data for mangrove species classification // Proceedings of the 1st International Conference on Applied Algorithms. Kolkata, India: Springer: 223–236
Chang C I and Du Q. 2004. Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 608–619
Chen J, Richard C and Honeine P. 2013. Nonlinear unmixing of hyperspectral data based on a linear-mixture/nonlinear-fluctuation model. IEEE Transactions on Signal Processing, 61(2): 480–492
Clark R N, Swayze G A, Livo K E, Kokaly R F, Sutley S J, Dalton J B, McDougal R R and Gent C A. 2003. Imaging spectroscopy: earth and planetary remote sensing with the USGS Tetracorder and expert systems. Journal of Geophysical Research: Planets 108(E12): 5131
Clark R N, Swayze G A, Wise R, Livo K E, Hoefen T M, Kokaly R F and Sutley S J. 2007. USGS digital spectral library splib06a. US: Geological Survey.
Das S and Suganthan P N. 2011. Differential evolution: a survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1): 4–31
Eches O and Guillaume M. 2014. A Bilinear–Bilinear nonnegative matrix factorization method for hyperspectral unmixing. IEEE Geoscience and Remote Sensing Letters, 11(4): 778–782
Fan W Y, Hu B X, Miller J and Li M Z. 2009. Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data. International Journal of Remote Sensing, 30(11): 2951–2962
Ganseman J and Scheunders P. 2012. A jump start for NMF with N-FINDR and NNLS.
Giacobini M, Brabazon A, Cagoni S, Di Caro G A, Drechsler R, Farooq M, Fink A, Lutton E, Machado P, Minner S, O'Neill M, Romero J, Rothlauf F, Squillero G, Takagi H, Uyar A S and Yang S. 2007. Applications of Evolutionary Computing: EvoWorkshops 2007. Berlin Heidelberg: Springer-Verlag
Halimi A, Altmann Y, Dobigeon N and Tourneret J Y. 2011. Nonlinear unmixing of hyperspectral images using a generalized bilinear model. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4153–4162
Hapke B. 1981. Bidirectional reflectance spectroscopy: 1. Theory. Journal of Geophysical Research: Solid Earth (1978–2012), 86(B4): 3039–3054
Heinz D C and Chang C I. 2001. Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(3): 529–545
Heylen R, Burazerović D and Scheunders P. 2011. Non-linear spectral unmixing by geodesic simplex volume maximization. IEEE Journal of Selected Topics in Signal Processing, 5(3): 534–542
Kitayama S, Arakawa M and Yamazaki K. 2011. Differential evolution as the global optimization technique and its application to structural optimization. Applied Soft Computing, 11(4): 3792–3803
Luukka P and Lampinen J. 2011. Differential evolution classifier in noisy settings and with interacting variables. Applied Soft Computing, 11(1): 891–899
Meganem I, Deliot P, Briottet X, Deville Y and Hosseini S. 2014a. Linear–quadratic mixing model for reflectances in urban environments. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 544–558
Meganem I, Deville Y, Hosseini S, Déliot P and Briottet X. 2014b. Linear-quadratic blind source separation using NMF to unmix urban hyperspectral images. IEEE Transactions on Signal Processing, 62(7): 1822–1833
Mendes R and Mohais A S. 2005. DynDE: a differential evolution for dynamic optimization problems // Proceedings of the 2005 IEEE Congress on Evolutionary Computation. Edinburgh, Scotland: IEEE: 2808–2815
Miao L D and Qi H R. 2007. Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization. IEEE Transactions on Geoscience and Remote Sensing, 45(3): 765–777
Nascimento J M and Bioucas-Dias J M. 2009. Nonlinear mixture model for hyperspectral unmixing // Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770I. Berlin, Germany: SPIE
Nascimento J M P and Dias J M B. 2005. Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43(4): 898–910
Price K, Storn R M and Lampinen J A. 2005. Differential Evolution: A Practical Approach to Global Optimization. Berlin Heidelberg: Springer.
Pu H Y, Chen Z, Wang B and Xia W. 2015. Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1287–1303
覃事银, 罗文斐, 杨斌, 张锐豪. 2015.单形体体积最小化的差分进化光谱解混算法. 中国图象图形学报, 20(11): 1535–1544
Qin S Y, Luo W F, Yang B and Zhang R H. 2015. Simplex volume minimization based differential evolution algorithm for spectral unmixing. Journal of Image and Graphics, 20(11): 1535–1544
Somers B, Zortea M, Plaza A and Asner G P. 2012. Automated extraction of image-based endmember bundles for improved spectral unmixing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2): 396–408
Storn R and Price K. 1995. Differential evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces. Berkeley: ICSI
Vesterstrøm J and Thomsen R. 2004. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems // Proceedings of the Congress on Evolutionary Computation. Portland, OR, USA: IEEE: 1980–1987
Winter M E. 1999. N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyperspectral data // Proc. SPIE 3753, Imaging Spectrometry V. Denver, CO, USA: SPIE: 266–275
Yang Z Y, Tang K and Yao X. 2008. Large scale evolutionary optimization using cooperative coevolution. Information Sciences, 178(15): 2985–2999
Yu Y, Guo S and Sun W D. 2007. Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images // Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications. Wuhan, China: SPIE
张贤达. 2004.矩阵分析与应用. 北京: 清华大学出版社: 71–100
Zhang X D. 2004. Matrix Analysis and Applications. Beijing: Tsinghua University Press: 71–100
Zhong Y F, Zhao L and Zhang L P. 2014. An adaptive differential evolution endmember extraction algorithm for hyperspectral remote sensing imagery. IEEE Geoscience and Remote Sensing Letters, 11(6): 1061–1065
Zhuang L, Zhang B, Gao L R, Li J and Plaza A. 2015. Normal endmember spectral unmixing method for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2598–2606
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