浏览全部资源
扫码关注微信
纸质出版日期： 2020-04-07 ，
扫 描 看 全 文
韩竹,高连如,张兵,孙旭,李庆亭.2020.高分五号高光谱图像自编码网络非线性解混.遥感学报,24(4): 388-400
Han Zhu,Gao Lianru,Zhang Bing,Sun Xu,Li Qingting. 2020. Nonlinear hyperspectral unmixing algorithm based on deep autoencoder networks. Journal of Remote Sensing(Chinese). 24(4): 388-400
针对高光谱非线性混合模型中的共线性问题，提出了一种非监督的增强型非线性自编码网络方法ENAE（Enhanced Nonlinear Autoencoder）。通过结合自编码网络在挖掘数据内在结构、提取特征方面的优势，引入端元正则项减弱端元间的共线性效应，从而提高高光谱混合像元分解精度。ENAE方法的实现步骤主要包括两部分：一是网络结构初始化，二是非线性分解。网络结构初始化是确定编码器的节点数以及端元和丰度的初值；非线性分解则主要是实现损失函数的最小化。通过模拟数据、城市区域真实数据和高分五号卫星高光谱数据的实验，得到了相较于传统非线性分解方法更高的精度，证明了ENAE方法的鲁棒性。
The core of unmixing in hyperspectral images is to determine the mathematical form of the spectral mixing model in accordance with the radiative transmission characteristics of ground features and then obtain the endmember spectrum and abundance results. A nonlinear model is suitable for real mixed scenes because of the complexity of feature scenes. The accuracy of unmixing can be significantly improved by combining the advantages of autoencoder in internal structure data mining and feature extraction. However
this method cannot consider the collinearity of the model
resultingin overfittingand is sensitive to noise. This study proposed an unsupervised Enhanced Nonlinear AutoEncoder (ENAE) method. The introduction of endmember regularization reduces the collinearity between endmembers
thereby improving the accuracy of hyperspectral unmixing.
The implementation steps of the ENAE method include two phases
where the first phaseis the initialization of the network structure
and the second phase is the nonlinear unmixing. The initialization phase determinesthe number of nodes of the encoder and initial value of endmember and abundance
and the nonlinear unmixing phase mainly realizesthe minimization of the loss function. The initialization of endmember and abundance aims to rapidly make the loss function converge. The objective function of the ENAE method includes the mean square error between the reconstructed and original images and endmember regularization. L2 regularization is used to constrain the weight of endmembers for enabling the ENAE to learn the nonlinear effect in the nonlinear mixing model. In the entire network iteration
the ENAE method is a self-learning process and does not require the participation of prior knowledge. Therefore
the ENAE method is an unsupervised nonlinear unmixing method.
Experiments are conducted to validate the effectiveness of the proposed method. The experimental dataset includes simulation
urban real
and GF-5 satellite data. Three accuracy evaluation indices
namely
spectral angle distance
root mean square error of abundance
and image reconstruction error
are used to evaluate the effect of unmixing performance. Compared with the traditional nonlinear unmixing method
the deep learning method is superior in terms of endmember extraction and abundance estimation. The ENAE method can obtain high unmixing accuracy in the deep learning method
thereby provingthe effectiveness and robustness of the proposed method.
The collinearity problem between endmembers is reduced by introducing the endmember regularization constraint in the autoencoder
thereby improving the accuracy of unmixing in hyperspectral images. In future work
we will introduce noise reduction
sparsity
and spatial information to improve the method and focus on obtaining the actual value of the pure ground spectrum to study the unmixing algorithm in hyperspectral images
which will be valuable in improving the application capabilities of hyperspectral remote sensing satellites in China. With the development of deep learning method interpretability research
exploring nonlinear unmixing methods withmany explanatory and physical meanings will be investigated forhyperspectral images withmixed pixel.
高光谱遥感高分五号卫星自编码网络混合像元非线性分解
hyperspectral remote sensingGF-5 satelliteautoencodermixed pixelnonlinear unmixing
Altmann Y, Halimi A, Dobigeon 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 [DOI: 10.1109/TIP.2012.2187668http://dx.doi.org/10.1109/TIP.2012.2187668 ]
Bioucas-Dias J M,Nascimento J M P . 2008. Hyperspectral subspace identification. IEEE Transactions on Geoscience and Remote Sensing, 46(8): 2435-2445 [DOI: 10.1109/TGRS.2008.918089http://dx.doi.org/10.1109/TGRS.2008.918089 ]
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 [DOI: 10.1109/TSP.2012.2222390http://dx.doi.org/10.1109/TSP.2012.2222390 ]
Chen X H, Chen J, Jia X P, Somers B, Wu J and Coppin P . 2011. A quantitative analysis of virtual endmembers’ increased impact on the collinearity effect in spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 49(8): 2945-2956 [DOI: 10.1109/TGRS.2011.2121073http://dx.doi.org/10.1109/TGRS.2011.2121073 ]
Cui G X and Li D K . 2018. Overview on deep learning based on automatic encoder algorithms. Computer Systems and Applications, 27(9): 47-51
崔广新, 李殿奎 . 2018. 基于自编码算法的深度学习综述. 计算机系统应用, 27(9): 47-51） [DOI: 10.15888/j.cnki.csa.006542http://dx.doi.org/10.15888/j.cnki.csa.006542 ]
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 [DOI: 10.1080/01431160802558659http://dx.doi.org/10.1080/01431160802558659 ]
Guo R, Wang W and Qi H R . 2015. Hyperspectral image unmixing using autoencoder cascade//Proceedings of the 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). Tokyo, Japan: IEEE: 1-4 [DOI: 10.1109/WHISPERS.2015.8075378]
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 [DOI: 10.1109/TGRS.2010.2098414http://dx.doi.org/10.1109/TGRS.2010.2098414 ]
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 [DOI: 10.1109/36.911111http://dx.doi.org/10.1109/36.911111 ]
Heylen R, Burazerovic 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 [DOI: 10.1109/JSTSP.2010.2088377http://dx.doi.org/10.1109/JSTSP.2010.2088377 ]
Heylen R and Scheunders P . 2016. A multilinear mixing model for nonlinear spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 54(1): 240-251 [DOI: 10.1109/TGRS.2015.2453915http://dx.doi.org/10.1109/TGRS.2015.2453915 ]
Li J, Li XR, Huang B and Zhao LY . 2016. Hopfield neural network approach for supervised nonlinear spectral unmixing. IEEE Geoscience and Remote Sensing Letters, 13(7): 1002-1006 [DOI: 10.1109/LGRS.2016.2560222http://dx.doi.org/10.1109/LGRS.2016.2560222 ]
Licciardi G A and Del Frate F . 2011. Pixel unmixing in hyperspectral data by means of neural networks. IEEE Transactions on Geoscience and Remote Sensing, 49(11): 4163-4172 [DOI: 10.1109/TGRS.2011.2160950http://dx.doi.org/10.1109/TGRS.2011.2160950 ]
Nascimento J M P,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 [DOI: 10.1109/TGRS.2005.844293http://dx.doi.org/10.1109/TGRS.2005.844293 ]
Ozkan S, Kaya B and Akar G B . 2019. EndNet: Sparse autoencoder network for endmember extraction and hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 482-496 [DOI: 10.1109/TGRS.2018.2856929http://dx.doi.org/10.1109/TGRS.2018.2856929 ]
Palsson B, Sigurdsson J, Sveinsson J R and Ulfarsson M O . 2018. Hyperspectral unmixing using a neural network autoencoder. IEEE Access, 6: 25646-2565 6 [DOI: 10.1109/ACCESS.2018.2818280http://dx.doi.org/10.1109/ACCESS.2018.2818280 ]
Qu Y,Qi H R . 2019. uDAS: An untied denoising autoencoder with sparsity for spectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1698-1712 [DOI: 10.1109/TGRS.2018.2868690http://dx.doi.org/10.1109/TGRS.2018.2868690 ]
Raksuntorn N and Du Q . 2010. Nonlinear spectral mixture analysis for hyperspectral imagery in an unknown environment. IEEE Geoscience and Remote Sensing Letters, 7(4): 836-840 [DOI: 10.1109/LGRS.2010.2049334http://dx.doi.org/10.1109/LGRS.2010.2049334 ]
Somers B, Cools K, Delalieux S, Stuckens J, Van der Zande D, Verstraeten W W and Coppin P . 2009. Nonlinear hyperspectral mixture analysis for tree cover estimates in orchards. Remote Sensing of Environment, 113(6): 1183-1193 [DOI: 10.1016/j.rse.2009.02.003http://dx.doi.org/10.1016/j.rse.2009.02.003 ]
Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R . 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15: 1929-1958
Su Y C, Li J, Plaza A, Marinoni A, Gamba P and Chakravortty S . 2019. DAEN: deep autoencoder networks for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4309-4321 [DOI: 10.1109/TGRS.2018.2890633http://dx.doi.org/10.1109/TGRS.2018.2890633 ]
Su Y C, Marinoni A, Li J, Plaza A and Gamba P . 2017. Nonnegative sparse autoencoder for robust endmember extraction from remotely sensed hyperspectral images//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Fort Worth, TX, USA: IEEE: 205-208 [DOI: 10.1109/IGARSS.2017.8126930]
Taleb A and Jutten C . 1999. Source separation in post-nonlinear mixtures. IEEE Transactions on Signal Processing, 47(10): 2807-2820 [DOI: 10.1109/78.790661http://dx.doi.org/10.1109/78.790661 ]
Tong Q X, Zhang B, Zheng L F . 2006. Hyperspectral Remote Sensing. Beijing: Higher Education Press: 1-3
童庆禧, 张兵, 郑兰芬 . 2006. 高光谱遥感: 原理、技术与应用. 北京: 高等教育出版社: 1-3
Wang M, Zhao M, Chen J and Rahardja S . 2019. Nonlinear unmixing of hyperspectral data via deep autoencoder networks. IEEE Geoscience and Remote Sensing Letters, 16(9): 1467-1471 [DOI: 10.1109/LGRS.2019.2900733http://dx.doi.org/10.1109/LGRS.2019.2900733 ]
Wu B, Zhang L P and Li P X . 2006. Unmixing hyperspectral imagery based on support vector nonlinear approximating regression. Journal of Remote Sensing, 10(3): 312-318
吴波, 张良培, 李平湘 . 2006. 基于支撑向量回归的高光谱混合像元非线性分解. 遥感学报, 10(3): 312-318） [DOI: 10.1184/jrs.20060348http://dx.doi.org/10.1184/jrs.20060348 ]
Yang B, Wang B and Wu Z M . 2018. Unsupervised nonlinear hyperspectral unmixing based on bilinear mixture models via geometric projection and constrained nonnegative matrix factorization. Remote Sensing, 10(5): 801 [DOI: 10.3390/rs10050801http://dx.doi.org/10.3390/rs10050801 ]
Yuan B . 2018. NMF hyperspectral unmixing algorithm combined with spatial and spectral correlation analysis. Journal of Remote Sensing, 22(2): 265-276
袁博 . 2018. 空间与谱间相关性分析的NMF高光谱解混. 遥感学报, 22(2): 265-276） [DOI: 10.11834/jrs.20186445http://dx.doi.org/10.11834/jrs.20186445 ]
Zhang B and Sun X . 2015. Hyperspectral Images Unmixing Algorithm. Beijing: Science Press (张兵, 孙旭. 2015. 高光谱图像混合像元分解. 北京: 科学出版社)
Zhang X R, Sun Y J, Zhang J Y, Wu P and Jiao L C . 2018. Hyperspectral unmixing via deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 15(11): 1755-1759 [DOI: 10.1109/LGRS.2018.2857804http://dx.doi.org/10.1109/LGRS.2018.2857804 ]
相关作者
相关机构
京公网安备11010802024621