Précédent|Prochain

Hyperspectral anomaly detection using column-wise robust principal component analysis

  • role： First author第一作者
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan 430079, China

  • Email：sunweiwei@nbu.edu.cn
  • Introduction：孙伟伟(1985—　)，男，副教授，研究方向为地理信息系统、遥感理论和方法及“3S”技术在海岸带资源管理与环境变化监测中的应用。E-mail：sunweiwei@nbu.edu.cn
Weiwei SUN12,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

Fei LI1,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

Gang YANG1,  
  • Affiliation：

    Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China

Dianfa ZHANG1

résumé

Hyperspectral imagery (HSI) collects the detailed spectral response of ground objects on the Earth’s surface by using hundreds of narrow bands and presents a great potential for use in detecting anomalies (i.e., small and low-probability ground objects) from the main background. Sparsity theory has recently attracted increasing interest because of intelligent processing in the hyperspectral field. Many sparsity-based anomaly detection methods have been proposed in literature, and robust principal component analysis (RPCA)-based detectors are typical examples. However, regular RPCA-based anomaly detectors show that the sparse anomaly matrix should not be of low rank, and its nonzero entries should be randomly scattered in the matrix. This condition negatively affects the estimation of a low-rank background matrix and seriously impacts the effective separation between the background and anomalies in the image scene. Moreover, RPCA-based approaches involve many iteration procedures of primal variables, which leads to high computational complexity. Therefore, this study proposed column-wise RPCA (CWRPCA) to resolve these problems and improve the detection results. CWRPCA assumes that background information exists in low-dimensional randomized column subspace and possesses low-rank properties and that the anomalies are sparse and do not lie in the column subspace of the background. CWRPCA aims to decompose the HSI data matrix into the sum of a low-rank background matrix, a column-sparse anomaly matrix with small portions of nonzero columns, and a noise matrix. In this study, when the column subspace of the background was determined, anomalies were estimated from the nonzero columns of the anomaly matrix. The problem of determining the background and anomaly matrices was formulated into a convex optimization program in (2). The inexact augmented Lagrange multiplier algorithm was implemented to optimize the objective function. This algorithm introduces two auxiliary variables into the objective function and iteratively updates primal variables by fixing other variables. The low-rank background and sparse anomaly matrices were obtained when the iteration procedure terminated. The detection result was achieved by segmenting the L2 norms of column vectors in the anomaly matrix. Two HSI datasets were used to verify the detection performance of CWRPCA. The Receiver Operating Characteristic (ROC) curve and Area Under Curve (AUC) were utilized to evaluate detection performance. ROC describes the probabilities of detection and false alarm. AUC quantifies the area under the ROC curve and shows how far the ROC curve is from the baseline. The detection results of the proposed method were compared with those of five state-of-the-art anomaly detection methods, namely, low-rank and sparse matrix decomposition-based anomaly detection method, global Reed-Xiaoli method, dual window-based eigen separation transform, collaborative representation-based detector, and low-rank and sparse representation. Experimental results showed that the proposed CWRPCA outperformed the five state-of-the-art anomaly detection methods in temrs of ROC curve and AUC with a moderate computation cost. CWRPCA is better in detecting anomalies than other methods and can be a good alternative for hyperspectral anomaly detection.

mots-clés

hyperspectral imagery;anomaly detection;robust principal component analysis;column sparse;inexact augmented Laplacian multiplier

References

  1. 1.
    Candes E J, Li X D, Ma Y and Wright J. 2009. Robust principal com-ponent analysis? arXiv:0912.3599
  2. 2.
    Chen Y D, Xu H, Caramanis C and Sanghavi S. 2011. Robust matrix completion and corrupted columns//Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA: ICML: 873–880.
  3. 3.
    Cui X Q, Tian Y, Weng L B and Yang Y P. 2014. Anomaly detection in hyperspectral imagery based on low-rank and sparse decomposition//Proceedings of SPIE Volume 9069, Fifth International Conference on Graphic and Image Processing. Hong Kong, China: SPIE: 90690R [DOI: 10.1117/12.2050229]
  4. 4.
    Du P J, Xia J S, Xue C H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236–256 (
  5. 5.
    Kwon H, Der S Z and Nasrabadi N M. 2003. Adaptive anomaly detection using subspace separation for hyperspectral imagery. Optical Engineering, 42(11): 3342–3351
  6. 6.
    Li W and Du Q. 2015. Collaborative representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1463–1474
  7. 7.
    Lin Z, Ganesh A, Wright J, Wu L, Chen M and Ma Y. 2009. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix. Proceedings in International workshops on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 1–18 [DOI: 10.1.1.231.4169]
  8. 8.
    Lin Z C, Chen M M and Ma Y. 2010. The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv:1009.5055
  9. 9.
    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
  10. 10.
    Liu W M and Chang C I. 2013. Multiple-window anomaly detection for hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 644–658
  11. 11.
    Nasrabadi N M. 2014. Hyperspectral target detection: an overview of current and future challenges. IEEE Signal Processing Magazine, 31(1): 34–44
  12. 12.
    Rahmani M and Atia G. 2015. Randomized robust subspace recovery for high dimensional data matrices. arXiv:1505.05901
  13. 13.
    Reed I S and Yu X. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10): 1760–1770
  14. 14.
    Sun W W, Liu C, Li J L, Lai Y M and Li W Y. 2014. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery. Journal of Applied Remote Sensing, 8(1): 083641
  15. 15.
    Sun W W, Yang G, Du B, Zhang L F and Zhang L P. 2017. A sparse and low-rank near-isometric linear embedding method for feature extraction in hyperspectral imagery classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 4032–4046
  16. 16.
    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 (
  17. 17.
    Wang Y M, Jia J X, He Z P and Wang J Y. 2016. Key technologies of advanced hyperspectral imaging system. Journal of Remote Sensing, 20(5): 850–857 (
  18. 18.
    Wu Z B, Li Y L, Plaza A, Li J, Xiao F and Wei Z H. 2016. Parallel and distributed dimensionality reduction of hyperspectral data on cloud computing architectures. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6): 2270–2278
  19. 19.
    Xu H, Caramanis C and Sanghavi S. 2010. Robust PCA via outlier pursuit//Proceedings of the 23rd International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates Inc.: 2496–2504
  20. 20.
    Xu Y, Wu Z B, Li J, Plaza A and Wei Z H. 2016. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 1990–2000
  21. 21.
    Zhang L P, Du B and Zhong Y F. 2010. Hybrid detectors based on selective endmembers. IEEE Transactions on Geoscience and Remote Sensing, 48(6): 2633–2646
  22. 22.
    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 (
  23. 23.
    Zhang Y X, Du B and Zhang L P. 2015. A sparse representation-based binary hypothesis model for target detection in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1346–1354
  24. 24.
    Zhao R, Du B and Zhang L P. 2014. A robust nonlinear hyperspectral anomaly detection approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(4): 1227–1234

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website