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Received：14 August 2021，
Published：07 June 2022
移动端阅览
受仪器和观测条件限制，高光谱数据易受噪声污染，给数据解译带来挑战。针对传统稀疏解混模型抗噪性能差的问题，本文提出一种截断加权核范数稀疏解混方法，利用高光谱图像像元之间的相关性减轻噪声对丰度估计的干扰。该方法借助低秩表示在挖掘数据内在低维结构方面的优势，在稀疏解混中加入基于截断加权核范数的低秩约束，并结合加权稀疏技术，在稀疏正则项中引入空间邻域权重。截断加权核范数对丰度矩阵的奇异值向量分段处理，可以更好地实现丰度矩阵的低秩逼近，使丰度图像保持空间一致性并保留更多细节信息，空间加权策略则增强了丰度图像的空间连续性。模拟高光谱数据、Cuprite矿区真实数据和红树林高光谱数据实验表明，与其他先进的稀疏解混方法相比，所提方法具有更好的抗噪性，能够提高解混精度。
Spectral unmixing is an important technology for quantitative analysis of hyperspectral images
which estimates the pure source signal (endmember) and the corresponding fractional proportion (abundance). Sparse unmixing is one of the research highlights in the field of spectral unmixing. Sparse unmixing finds a set of endmembers that can optimally model mixed pixels from a known spectral library and takes the fractional abundance as the weight
thereby circumventing the process of endmember extraction. However
hyperspectral data are often contaminated by noise due to the limitations of instruments and observation conditions. This state is disadvantageous to data interpretation. Sparse unmixing is peculiarly prone to be disturbed by noise and thus affect the accuracy of abundance estimation or even erroneously identify endmembers from spectral libraries.
To overcome this drawback
this study proposes a hyperspectral sparse unmixing method with truncated weighted nuclear norm
which exploits the correlation of pixels to reduce the interference of noise on abundance estimation. The proposed method adds the low-rank constraint based on truncated weighted nuclear norms to the sparse unmixing model given that low-rank representation is available to mine the inherent low-dimensional structure of data. It is different from other nuclear norm minimization
singular values are divided into two groups and treated with the truncated nuclear norm and weighted kernel norm. It provides a better low-rank approximation of the abundance matrix
which maintains the spatial consistency of image and protects the detailed information. Inspired by the weighted sparse strategy
the spatial neighborhood weight is introduced into the sparse regularization term
which enhances the spatial continuity of image. The underlying optimization problem is solved by the alternating direction method of multipliers efficiently.
Experiments are conducted on simulated data
real Cuprite
and mangrove hyperspectral data to verify the unmixing performance of the algorithm. In particular
there is no available spectral library for mangrove hyperspectral data
which is essential for the sparse unmixing algorithm
so a spectral library is built derived from the original data. The various vegetation curves in the library are relatively close
which brings challenges to the unmixing task. Even so
the proposed method identified all mangrove species
and achieved approximately consistent results with the reference classification map. Compared with other advanced sparse unmixing methods
the proposed method is superior in restraining the influence of noise and can obtain high unmixing accuracy even in the case of high noise.
In future work
we will further explore the spatial information of hyperspectral images with tensor-based low-rank representation to improve the robustness of the sparse unmixing algorithm. In addition
we will collect hyperspectral data with more mangrove species and expand the corresponding spectral library
further develop mangrove species classification techniques based on spectral unmixing to better serve the investigation of mangrove species composition.
Ahmad T , Lyngdoh R B , Sahadevan A S , Raha S , Gupta P K and Misra A . 2020 . Four-directional spatial regularization for sparse hyperspectral unmixing . Journal of Applied Remote Sensing , 14 ( 4 ): 046511 [ DOI: 10.1117/1.JRS.14.046511 http://dx.doi.org/10.1117/1.JRS.14.046511 ]
Bioucas-Dias J M and Nascimento J M P . 2008 . Hyperspectral subspace identification . IEEE Transactions on Geoscience and Remote Sensing , 46 ( 8 ): 2435 - 2445 [ DOI: 10.1109/TGRS.2008.918089 http://dx.doi.org/10.1109/TGRS.2008.918089 ]
Cai J F , Candes E J and Shen Z W . 2010 . A singular value thresholding algorithm for matrix completion . SIAM Journal on Optimization , 20 ( 4 ): 1956 - 1982 [ DOI: 10.1137/080738970 http://dx.doi.org/10.1137/080738970 ]
Candes E J and Tao T . 2005 . Decoding by linear programming . IEEE Transactions on Information Theory , 51 ( 12 ): 4203 - 4215 [ DOI: 10.1109/TIT.2005.858979 http://dx.doi.org/10.1109/TIT.2005.858979 ]
Candes E J and Tao T . 2006 . Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Transactions on Information Theory , 52 ( 12 ): 5406 - 5425 [ DOI: 10.1109/TIT.2006.885507 http://dx.doi.org/10.1109/TIT.2006.885507 ]
Candès E J , Wakin M B and Boyd S P . 2008 . Enhancing sparsity by reweighted ℓ 1 minimization . Journal of Fourier Analysis and Applications , 14 ( 5 ): 877 - 905 [ DOI: 10.1007/s00041-008-9045-x http://dx.doi.org/10.1007/s00041-008-9045-x ]
Cao J J , Leng W C , Liu K , Liu L , He Z , and Zhu Y H . 2018a . Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models . Remote Sensing , 10 ( 1 ): 89 [ DOI: 10.3390/rs10010089 http://dx.doi.org/10.3390/rs10010089 ]
Cao J J , Liu K , Liu L , Zhu Y H , Li J and He Z . 2018b . Identifying mangrove species using field close-range snapshot hyperspectral imaging and machine-learning techniques . Remote Sensing , 10 ( 12 ): 2047 [ DOI: 10.3390/rs10122047 http://dx.doi.org/10.3390/rs10122047 ]
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 [ DOI: 10.1109/TGRS.2003.819189 http://dx.doi.org/10.1109/TGRS.2003.819189 ]
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 , 108 ( E12 ): 5131 [ DOI: 10.1029/2002JE001847 http://dx.doi.org/10.1029/2002JE001847 ]
Eches O , Dobigeon N and Tourneret J Y . 2011 . Enhancing Hyperspectral Image Unmixing With Spatial Correlations . IEEE Transactions on Geoscience and Remote Sensing , 49 ( 11 ): 4239 - 4247 [ DOI: 10.1109/TGRS.2011.2140119 http://dx.doi.org/10.1109/TGRS.2011.2140119 ]
Fazel M . 2001 . Matrix Rank Minimization with Applications . Stanford : Stanford University .
Giampouras P V , Themelis K E , Rontogiannis A A and Koutroumbas K D . 2016 . Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 8 ): 4775 - 4789 [ DOI: 10.1109/TGRS.2016.2551327 http://dx.doi.org/10.1109/TGRS.2016.2551327 ]
Gu S H , Xie Q , Meng D Y , Zuo W M , Feng X C and Zhang L . 2017 . Weighted nuclear norm minimization and its applications to low level vision . International Journal of Computer Vision , 121 ( 2 ): 183 - 208 [ DOI: 10.1007/s11263-016-0930-5 http://dx.doi.org/10.1007/s11263-016-0930-5 ]
He Z , Shi Q , Liu K , Cao J J , Zhan W and Cao B F . 2020 . Object-oriented mangrove species classification using hyperspectral data and 3-D siamese residual network . IEEE Geoscience and Remote Sensing Letters , 17 ( 12 ): 2150 - 2154 [ DOI: 10.1109/LGRS.2019.2962723 http://dx.doi.org/10.1109/LGRS.2019.2962723 ]
Huang J , Huang T Z , Deng L J and Zhao X L . 2019 . Joint-sparse-blocks and low-rank representation for hyperspectral unmixing . IEEE Transactions on Geoscience and Remote Sensing , 57 ( 4 ): 2419 - 2438 [ DOI: 10.1109/TGRS.2018.2873326 http://dx.doi.org/10.1109/TGRS.2018.2873326 ]
Iordache M D . 2011 . A Sparse Regression Approach to Hyperspectral Unmixing . Lisboa : Universidade Técnica De Lisboa
Iordache M D , Bioucas-Dias J M and Plaza A . 2011 . Sparse unmixing of hyperspectral data . IEEE Transactions on Geoscience and Remote Sensing , 49 ( 6 ): 2014 - 2039 [ DOI: 10.1109/TGRS.2010.2098413 http://dx.doi.org/10.1109/TGRS.2010.2098413 ]
Iordache M D , Bioucas-Dias J M and Plaza A . 2012 . Total variation spatial regularization for sparse hyperspectral unmixing . IEEE Transactions on Geoscience and Remote Sensing , 50 ( 11 ): 4484 - 4502 [ DOI: 10.1109/TGRS.2012.2191590 http://dx.doi.org/10.1109/TGRS.2012.2191590 ]
Iordache M D , Bioucas-Dias J M and Plaza A . 2014 . Collaborative sparse regression for hyperspectral unmixing . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 1 ): 341 - 354 [ DOI: 10.1109/TGRS.2013.2240001 http://dx.doi.org/10.1109/TGRS.2013.2240001 ]
Keshava N and Mustard J F . 2002 . Spectral unmixing . IEEE Signal Processing Magazine , 19 ( 1 ): 44 - 57 [ DOI: 10.1109/79.974727 http://dx.doi.org/10.1109/79.974727 ]
Lan J H , Zou J L , Hao Y S , Zeng Y L , Zhang Y Z and Dong M W . 2018 . Research progress on unmixing of hyperspectral remote sensing imagery . Journal of Remote Sensing , 22 ( 1 ): 13 - 27
蓝金辉 , 邹金霖 , 郝彦爽 , 曾溢良 , 张玉珍 , 董铭巍 . 2018 . 高光谱遥感影像混合像元分解研究进展 . 遥感学报 , 22 ( 1 ): 13 - 27 [ DOI: 10.11834/jrs.20186502 http://dx.doi.org/10.11834/jrs.20186502 ]
Liu Y N . 2021 . Development of hyperspectral imaging remote sensing technology . Journal of Remote Sensing , 25 ( 1 ): 439 - 459
刘银年 . 2021 . 高光谱成像遥感载荷技术的现状与发展 . 遥感学报 , 25 ( 1 ): 439 - 459 [ DOI: 10.11834/jrs.20210283 http://dx.doi.org/10.11834/jrs.20210283 ]
Niu A Y , Chen Z Y , Xu S J , Xu G C , Yang Q and Ma J J . 2016 . LUCC-Based dynamic evaluation of ecosystem service value in Qi’ao Island, Zhuhai . Journal of South China Normal University (Natural Science Edition) , 48 ( 2 ): 81 - 87
牛安逸 , 陈志云 , 徐颂军 , 许观嫦 , 杨倩 , 马姣娇 . 2016 . 基于LUCC的珠海淇澳岛生态系统服务功能价值动态评估 . 华南师范大学学报(自然科学版) , 48 ( 2 ): 81 - 87 [ DOI: 10.6054/j.jscnun.2015.12.005 http://dx.doi.org/10.6054/j.jscnun.2015.12.005 ]
Pan S H and Wen Z W . 2020 . Models and algorithms for low-rank and sparse matrix optimization problems . Operations Research Transactions , 24 ( 3 ): 1 - 26
潘少华 , 文再文 . 2020 . 低秩稀疏矩阵优化问题的模型与算法 . 运筹学学报 , 24 ( 3 ): 1 - 26 [ DOI: 10.15960/j.cnki.issn.1007-6093.2020.03.001 http://dx.doi.org/10.15960/j.cnki.issn.1007-6093.2020.03.001 ]
Qu Q , Nasrabadi N M and Tran T D . 2014 . Abundance estimation for bilinear mixture models via joint sparse and low-rank representation . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 7 ): 4404 - 4423 [ DOI: 10.1109/TGRS.2013.2281981 http://dx.doi.org/10.1109/TGRS.2013.2281981 ]
Tang H L , Liu K , Zhu Y H , Wang S G , Liu L and Song S . 2015 . Mangrove community classification based on WorldView-2 image and SVM method . Acta Scientiarum Naturalium Universitatis Sunyatseni , 54 ( 4 ): 102 - 111
唐焕丽 , 刘凯 , 朱远辉 , 王树功 , 柳林 , 宋莎 . 2015 . 基于WorldView-2数据和支持向量机的红树林群落分类研究 . 中山大学学报(自然科学版) , 54 ( 4 ): 102 - 111 [ DOI: 10.13471/j.cnki.acta.snus.2015.04.020 http://dx.doi.org/10.13471/j.cnki.acta.snus.2015.04.020 ]
Tong Q X , Zhang B and Zhang L F . 2016 . Current progress of hyperspectral remote sensing in China . Journal of Remote Sensing , 20 ( 5 ): 689 - 707
童庆禧 , 张兵 , 张立福 . 2016 . 中国高光谱遥感的前沿进展 . 遥感学报 , 20 ( 5 ): 689 - 707 [ DOI: 10.11834/jrs.20166264 http://dx.doi.org/10.11834/jrs.20166264 ]
Wang J J , Huang T Z , Huang J and Deng L J . 2021 . A two-step iterative algorithm for sparse hyperspectral unmixing via total variation . Applied Mathematics and Computation , 401 : 126059 [ DOI: 10.1016/j.amc.2021.126059 http://dx.doi.org/10.1016/j.amc.2021.126059 ]
Wang R , Li H C , Liao W Z and Pižurica A . 2016 . Double reweighted sparse regression for hyperspectral unmixing // Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium . Beijing : IEEE: 6986 - 6989 [ DOI: 10.1109/IGARSS.2016.7730822 http://dx.doi.org/10.1109/IGARSS.2016.7730822 ]
Zhang B . 2016 . Advancement of hyperspectral image processing and information extraction . Journal of Remote Sensing , 20 ( 5 ): 1062 - 1090
张兵 . 2016 . 高光谱图像处理与信息提取前沿 . 遥感学报 , 20 ( 5 ): 1062 - 1090 [ DOI: 10.11834/jrs.20166179 http://dx.doi.org/10.11834/jrs.20166179 ]
Zhang D B , Hu Y , Ye J P , Li X L and He X F . 2012 . Matrix completion by truncated nuclear norm regularization // Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition . Providence : IEEE: 2192 - 2199 [ DOI: 10.1109/CVPR.2012.6247927 http://dx.doi.org/10.1109/CVPR.2012.6247927 ]
Zhang L P and Li J Y . 2016 . Development and prospect of sparse representation-based hyperspectral image processing and analysis . Journal of Remote Sensing , 20 ( 5 ): 1091 - 1101
张良培 , 李家艺 . 2016 . 高光谱图像稀疏信息处理综述与展望 . 遥感学报 , 20 ( 5 ): 1091 - 1101 [ DOI: 10.11834/jrs.20166050 http://dx.doi.org/10.11834/jrs.20166050 ]
Zhang S Q , Li J , Li H C , Deng C Z and Plaza A . 2018 . Spectral-spatial weighted sparse regression for hyperspectral image unmixing . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 6 ): 3265 - 3276 [ DOI: 10.1109/TGRS.2018.2797200 http://dx.doi.org/10.1109/TGRS.2018.2797200 ]
Zhang S Q , Li J , Plaza J , Li H C and Plaza A . 2017 . Spatial weighted sparse regression for hyperspectral image unmixing // Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium . Fort Worth : IEEE: 225 - 228 [ DOI: 10.1109/IGARSS.2017.8126935 http://dx.doi.org/10.1109/IGARSS.2017.8126935 ]
Zhang S Y , Hua W S , Zhou B , Liu J , Li G and Wan L . 2021 . Two-step iterative row-sparsity hyperspectral unmixing via low-rank constraint . Journal of Applied Remote Sensing , 15 ( 4 ): 042602 [ DOI: 10.1117/1.JRS.15.042602 http://dx.doi.org/10.1117/1.JRS.15.042602 ]
Zheng J W , Lou K C , Yang X , Bai C and Tang J H . 2019 . Weighted mixed-norm regularized regression for robust face identification . IEEE Transactions on Neural Networks and Learning Systems , 30 ( 12 ): 3788 - 3802 [ DOI: 10.1109/TNNLS.2019.2899073 http://dx.doi.org/10.1109/TNNLS.2019.2899073 ]
Zheng J W , Qin M J , Zhou X L , Mao J F and Yu H C . 2020 . Efficient implementation of truncated reweighting low-rank matrix approximation . IEEE Transactions on Industrial Informatics , 16 ( 1 ): 488 - 500 [ DOI: 10.1109/TII.2019.2916986 http://dx.doi.org/10.1109/TII.2019.2916986 ]
Zhong Y F , Feng R Y and Zhang L P . 2014 . Non-local sparse unmixing for hyperspectral remote sensing imagery . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 7 ( 6 ): 1889 - 1909 [ DOI: 10.1109/JSTARS.2013.2280063 http://dx.doi.org/10.1109/JSTARS.2013.2280063 ]
Zhu C Y , Zhang S Q , Li J and Li H C . 2018 . Spatially weighted collaborative sparse unmixing for hyperspectral images . Journal of Nanjing University of Information Science and Technology (Natural Science Edition) , 10 ( 1 ): 92 - 101
朱昌宇 , 张绍泉 , 李军 , 李恒超 . 2018 . 基于空间加权协同稀疏的高光谱解混算法研究 . 南京信息工程大学学报(自然科学版) , 10 ( 1 ): 92 - 101 [ DOI: 10.13878/j.cnki.jnuist.2018.01.009 http://dx.doi.org/10.13878/j.cnki.jnuist.2018.01.009 ]
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