Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation

SU Hongjun; YAO Wenjing; WU Zhaoyue

doi:10.11834/jrs.20210209

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Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation
Vol. 26, Issue 11, Pages: 2354-2368(2022)
Published： 07 November 2022 ，
DOI： 10.11834/jrs.20210209

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苏红军，姚文静，吴曌月.2022.结合弹性网络与低秩表示的高光谱遥感影像分类方法.遥感学报，26（11）： 2354-2368

Su H J，Yao W J and Wu Z Y. 2022. Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation. National Remote Sensing Bulletin， 26（11）：2354-2368
苏红军，姚文静，吴曌月.2022.结合弹性网络与低秩表示的高光谱遥感影像分类方法.遥感学报，26（11）： 2354-2368 DOI： 10.11834/jrs.20210209.

Su H J，Yao W J and Wu Z Y. 2022. Hyperspectral remote sensing imagery classification based on elastic net and low-rank representation. National Remote Sensing Bulletin， 26（11）：2354-2368 DOI： 10.11834/jrs.20210209.

摘要

近年来，低秩表示LRR（Low-rank Representation）在高光谱遥感影像分类中的应用越来越广泛，如何利用LRR准确地对地物进行分类已成为高光谱遥感研究的一个挑战。针对以上问题，本文设计了基于弹性网络的低秩表示ENLRR（Low-rank Representation based on Elastic Net）方法，并将该算法扩展到了核空间提出了基于弹性网络的核低秩表示方法KENLRR（Kernel ENLRR）。低秩表示分类可以充分利用影像的全局信息，它的基本思想是利用尽可能少的训练样本的线性组合来表示整个测试影像，再通过表示系数和训练样本对目标影像进行恢复重建，并根据最小重构误差准则判断每个像素的类别。提出的ENLRR方法的基本思想是在LRR中引入弹性网络思想，利用系数矩阵的核范数和F-范数代替秩函数进行低秩优化求解。为了更好地解决非线性数据的分类问题，在ENLRR 方法中引入核函数，提出KENLRR方法，通过邻域滤波核函数将原数据映射到高维特征空间中，实现空谱联合分类，进一步提高分类精度。实验部分选用3组高光谱遥感数据，利用提出的算法与SVM、KNN、ELM、LRR、MFLRR、LSLRR和KLRR等对比算法进行地物分类。结果表明，提出的两种算法在高光谱遥感地物分类方面效果较好，而且具有良好的稳定性和适应性。与LRR算法相比，提出的算法在Washington DC数据集上的精度分别提高了4.55%和6.74%，在Purdue Campus数据集上的精度分别提高了14.22%和23.30%，在高分五号GF-5（Gaofen-5）黄河口湿地数据集上的精度分别提高了8.45%和15.40%，而且结果也表明KENLRR算法具有最佳的分类表现。精确的分类结果为分析地物分布格局提供了技术支撑，也证明了本文提出的两种算法在高光谱遥感影像分类上的优越性。

Abstract

Recently

Low-Rank Representation (LRR) has been widely used in hyperspectral remote sensing imagery classification. How to accurately classify ground objects by LRR has become a challenge in hyperspectral remote sensing research. The LRR based on elastic net (ENLRR) and the extended kernel version of ENLRR (KENLRR) are proposed to solve the above-mentioned problem. LRR classification method can make full use of the global information of the image. Its basic idea is to represent the whole test image by using the linear combination of as few training samples as possible

reconstructing the target image according to the representation coefficient matrix and training samples

and calculating the class of each pixel by the minimum reconstruction error criterion. The main idea of ENLRR is to introduce an elastic net into the LRR model

which replaces the rank function with the combination of nuclear and Frobenius norms of the coefficient matrix. To better classify nonlinear data

a modified KENLRR method is proposed by introducing kernel tricks in the ENLRR algorithm

and the neighborhood filter kernel function is adopted to map the original data into a high-dimensional feature space

which can obtain spatial-spectral joint information for better classification. In the experiments

three popular hyperspectral datasets are adopted

the proposed methods and the SVM

KNN

ELM

LRR

MFLRR

LSLRR

and KLRR comparison methods are used to carry out classification. Based on the experimental results

the proposed methods are effective in accurately distinguishing ground objects and have good stability and adaptability. In comparison with LRR method

the overall classification accuracies of ENLRR and KENLRR are improved by 4.55% and 6.74% in the Washington DC dataset

14.22% and 23.30% in the Purdue Campus dataset

and 8.45% and 15.40% in the Gaofen-5 (GF-5) Yellow River Delta dataset. Therefore

the KENLRR method can provide the best performance for hyperspectral remote sensing imagery classification. The high-quality classification results provide technical support for analyzing the distribution pattern of ground objects

and prove the superiority of the proposed methods in hyperspectral remote sensing imagery classification。

关键词

高光谱遥感低秩表示ENLRRKENLRR影像分类

Keywords

hyperspectral remote sensinglow-rank representationENLRRKENLRRimage classification

references

Altman N S. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3): 175-185 [DOI: 10.1080/00031305.1992.10475879http://dx.doi.org/10.1080/00031305.1992.10475879]

Baudat G and Anouar F. 2000. Generalized discriminant analysis using a kernel approach. Neural Computation, 12(10): 2385-2404 [DOI: 10.1162/089976600300014980http://dx.doi.org/10.1162/089976600300014980]

Chen C F, Wei C P and Wang Y C F. 2012. Low-rank matrix recovery with structural incoherence for robust face recognition//2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, RI, USA: IEEE: 2618-2625 [DOI: 10.1109/CVPR.2012.6247981http://dx.doi.org/10.1109/CVPR.2012.6247981]

Cui B G, Cui J D, Lu Y, Guo N N and Gong M G. 2020. A sparse representation-based sample pseudo-labeling method for hyperspectral image classification. Remote Sensing, 12(4): 664 [DOI: 10.3390/rs12040664http://dx.doi.org/10.3390/rs12040664]

Deng W and Yin W T. 2016. On the global and linear convergence of the generalized alternating direction method of multipliers. Journal of Scientific Computing, 66(3): 889-916 [DOI: 10.1007/s10915-015-0048-xhttp://dx.doi.org/10.1007/s10915-015-0048-x]

Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. National Remote Sensing Bulletin, 20(2):236-256

杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]

Huang G B, Zhu Q Y and Siew C K. 2006. Extreme learning machine: theory and applications. Neurocomputing, 70(1/3): 489-501 [DOI: 10.1016/J.NEUCOM.2005.12.126http://dx.doi.org/10.1016/J.NEUCOM.2005.12.126]

Huang X and Zhang L P. 2008. An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 46(12): 4173-4185 [DOI: 10.1109/TGRS.2008.2002577http://dx.doi.org/10.1109/TGRS.2008.2002577]

Jia S, Shen L L and Li Q Q. 2015. Gabor feature-based collaborative representation for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 53(2): 1118-1129 [DOI: 10.1109/TGRS.2014.2334608http://dx.doi.org/10.1109/TGRS.2014.2334608]

Jiao L L, Sun W W, Yang G, Ren G B and Liu Y N. 2019. A hierarchical classification framework of satellite multispectral/hyperspectral images for mapping coastal wetlands. Remote Sensing, 11(19): 2238 [DOI: 10.3390/rs11192238http://dx.doi.org/10.3390/rs11192238]

Liu G C, Lin Z C, Yan S C, Sun J, Yu Y and Ma Y. 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1): 171-184 [DOI: 10.1109/TPAMI.2012.88http://dx.doi.org/10.1109/TPAMI.2012.88]

Melgani F and Bruzzone L. 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42(8): 1778-1790 [DOI: 10.1109/TGRS.2004.831865http://dx.doi.org/10.1109/TGRS.2004.831865]

Peng J T, Sun W W, Wei T H and Fan W Q. 2020. A modified correlation alignment algorithm for the domain adaptation of GF-5 hyperspectral image. National Remote Sensing Bulletin, 24(4): 417-426

彭江涛, 孙伟伟, 魏天慧, 范文琦. 2020. 高分五号高光谱影像的关联对齐域适应与分类. 遥感学报, 24(4): 417-426 [DOI: 10.11834/jrs.20209212http://dx.doi.org/10.11834/jrs.20209212]

Ren K, Sun W W, Meng X C, Yang G and Du Q. 2020. Fusing China GF-5 hyperspectral data with GF-1, GF-2 and Sentinel-2A multispectral data: which methods should be used?. Remote Sensing, 12(5): 882 [DOI: 10.3390/rs12050882http://dx.doi.org/10.3390/rs12050882]

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing: Advances, challenges, and prospects. National Remote Sensing Bulletin, 26（8）: 1504-1529

苏红军. 2022. 高光谱遥感影像降维：进展、挑战与展望. 遥感学报，26（8）: 1504-1529 [DOI: 10.11834/jrs.20210354http://dx.doi.org/10.11834/jrs.20210354]

Su H J, Yu Y, Du Q and Du P J. 2020. Ensemble learning for hyperspectral image classification using tangent collaborative representation. IEEE Transactions on Geoscience and Remote Sensing, 58(6): 3778-3790 [DOI: 10.1109/TGRS.2019.2957135http://dx.doi.org/10.1109/TGRS.2019.2957135]

Su H J, Zhao B, Du Q, Du P J and Xue Z H. 2018. Multifeature dictionary learning for collaborative representation classification of hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 56(4): 2467-2484 [DOI: 10.1109/TGRS.2017.2781805http://dx.doi.org/10.1109/TGRS.2017.2781805]

Sun L, Ma C Y, Chen Y J, Zheng Y H, Shim H J, Wu Z B and Jeon B. 2020. Low rank component induced spatial-spectral kernel method for hyperspectral image classification. IEEE Transactions on Circuits and Systems for Video Technology, 30(10): 3829-3842 [DOI: 10.1109/TCSVT.2019.2946723http://dx.doi.org/10.1109/TCSVT.2019.2946723]

Sun W W, Yang G, Chen C, Chang M H, Huang K, Meng X Z and Liu L Y. 2020. Development status and literature analysis of China’s earth observation remote sensing satellites. National Remote Sensing Bulletin, 24(5): 479-510

孙伟伟, 杨刚, 陈超, 常明会, 黄可, 孟祥珍, 刘良云. 2020. 中国地球观测遥感卫星发展现状及文献分析. 遥感学报, 24(5): 479-510 [DOI: 10.11834/jrs.20209464http://dx.doi.org/10.11834/jrs.20209464]

Tong Q X, Zhang B and Zheng L F. 2006. Hyperspectral Remote Sensing: the Principle, Technology and Application. Beijing: Higher Education Press

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

Wang Q, He X and Li X L. 2019. Locality and structure regularized low rank representation for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 911-923 [DOI: 10.1109/TGRS.2018.2862899http://dx.doi.org/10.1109/TGRS.2018.2862899]

Wright J, Yang A Y, Ganesh A, Sastry S S and Ma Y. 2009. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2): 210-227 [DOI: 10.1109/TPAMI.2008.79http://dx.doi.org/10.1109/TPAMI.2008.79]

Wu F Y, Wang X, Ding J W, Du P J and Tan K. 2020. Improved cascade forest deep learning model for hyperspectral imagery classification. National Remote Sensing Bulletin, 24(4): 439-453

武复宇, 王雪, 丁建伟, 杜培军, 谭琨. 2020. 高光谱遥感影像多级联森林深度网络分类算法. 遥感学报, 24(4): 439-453 [DOI: 10.11834/jrs.20209190http://dx.doi.org/10.11834/jrs.20209190]

Yu H Y, Gao L R, Liao W Z, Zhang B, Zhuang L N, Song M P and Chanussot J. 2020. Global spatial and local spectral similarity-based manifold learning group sparse representation for hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 58(5): 3043-3056 [DOI: 10.1109/TGRS.2019.2947032http://dx.doi.org/10.1109/TGRS.2019.2947032]

Zhang L F，Zhao X Y，Sun X J，Huang H，Peng M Y，Cen Y and Tu K. 2022. Comparison of fusion methods on GF-5 hyperspectral data. National Remote Sensing Bulletin, 26（4）: 632-645

张立福, 赵晓阳, 孙雪剑, 黄海, 彭明媛, 岑奕, 涂宽. 2022. 高分五号高光谱数据融合方法比较. 遥感学报, 26（4）: 632-645 [DOI: 10.11834/jrs.20229318http://dx.doi.org/10.11834/jrs.20229318]

Zhang X Z, Wang Y J, Li D, Tan Z Y and Liu S J. 2018. Fusion of multifeature low-rank representation for synthetic aperture radar target configuration recognition. IEEE Geoscience and Remote Sensing Letters, 15(9): 1402-1406 [DOI: 10.1109/LGRS.2018.2842068http://dx.doi.org/10.1109/LGRS.2018.2842068]

Zou H and Hastie T. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2): 301-320 [DOI: 10.1111/j.1467-9868.2005.00503.xhttp://dx.doi.org/10.1111/j.1467-9868.2005.00503.x]

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