高光谱数据截断加权核范数稀疏解混

李璠; 张绍泉; 曹晶晶; 梁炳堃; 李军; 刘凯; 邓承志; 汪胜前

doi:10.11834/jrs.20221553

遥感智能解译 | 浏览量 : 0 下载量: 237 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

高光谱数据截断加权核范数稀疏解混
Sparse unmixing with truncated weighted nuclear norm for hyperspectral data
2022年26卷第6期页码：1067-1082
纸质出版日期： 2022-06-07 ，
DOI： 10.11834/jrs.20221553

扫描看全文

李璠，张绍泉，曹晶晶，梁炳堃，李军，刘凯，邓承志，汪胜前.2022.高光谱数据截断加权核范数稀疏解混.遥感学报，26（6）： 1067-1082

Li F，Zhang S Q，Cao J J，Liang B K，Li J，Liu K，Deng C Z and Wang S Q. 2022. Sparse unmixing with truncated weighted nuclear norm for hyperspectral data. National Remote Sensing Bulletin， 26（6）：1067-1082
李璠，张绍泉，曹晶晶，梁炳堃，李军，刘凯，邓承志，汪胜前.2022.高光谱数据截断加权核范数稀疏解混.遥感学报，26（6）： 1067-1082 DOI： 10.11834/jrs.20221553.

Li F，Zhang S Q，Cao J J，Liang B K，Li J，Liu K，Deng C Z and Wang S Q. 2022. Sparse unmixing with truncated weighted nuclear norm for hyperspectral data. National Remote Sensing Bulletin， 26（6）：1067-1082 DOI： 10.11834/jrs.20221553.

摘要

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。

Abstract

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.

关键词

遥感高光谱数据稀疏解混低秩正则化截断加权核范数空间权重

Keywords

remote sensinghyperspectral datasparse unmixinglow-rank regularizationtruncated weighted nuclear normspatial weight

references

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.14.046511http://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.2008.918089http://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: 10.1137/080738970http://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.2005.858979http://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.2006.885507http://dx.doi.org/10.1109/TIT.2006.885507]

Candès E J, Wakin M B and Boyd S P. 2008. Enhancing sparsity by reweighted ℓ1 minimization. Journal of Fourier Analysis and Applications, 14(5): 877-905 [DOI: 10.1007/s00041-008-9045-xhttp://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/rs10010089http://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/rs10122047http://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.2003.819189http://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: 10.1029/2002JE001847http://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.2011.2140119http://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.2016.2551327http://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/s11263-016-0930-5http://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.2019.2962723http://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.2018.2873326http://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.2010.2098413http://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.2012.2191590http://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.2013.2240001http://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: 10.1109/79.974727http://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.20186502http://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.20210283http://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.2015.12.005http://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.1007-6093.2020.03.001http://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.2013.2281981http://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.2015.04.020http://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.20166264http://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.2021.126059http://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.2016.7730822http://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.20166179http://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.2012.6247927http://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.20166050http://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.2018.2797200http://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.2017.8126935http://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.15.042602http://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.2019.2899073http://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.2019.2916986http://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.2013.2280063http://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.2018.01.009http://dx.doi.org/10.13878/j.cnki.jnuist.2018.01.009]

文章被引用时，请邮件提醒。

提交

基于高光谱卫星影像的生长期互花米草指数构建

透视地球——新一代对地观测技术

迁移深度卷积神经网络模型秋粮作物泛化识别

Grid A*：面向野外空地协同应急处置的快速路径规划