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Progressive line processing of global and local real-time anomaly detection in hyperspectral images

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Abstract

Hyperspectral imaging, which is characterized by its abundant spectral and spatial information, can effectively identify and detect ground objects. In order to detect moving targets and relieve the stress of big data storage, real-time processing of anomaly detection is greatly desired. This paper investigates both global and local real-time implementations of the most widely used RX detector in a line-by-line fashion. Firstly, global and local causal frameworks are designed to meet the causality, which is one requirement of real-time character. Secondly, taking advantage of the Woodbury matrix identity, recursive update equations of the inverse covariance matrix and background data estimate mean are derived, thereby achieving very low computational complexity. As for local real-time architecture, multiple local semi-windows are designed to simultaneously detect all pixels of a data line. This designation has an advantage that it is very beneficial for the implementation of real-time anomaly detection on graphics processing units. The proposed global and local real-time strategies have been deeply analyzed summarizing that the computational complexity is greatly reduced under the comparable detection accuracy. This is finally validated by experimental results.

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References

  1. Chang, C.-I.: Hyperspectral Imaging: Techniques for Spectral Detection and Classification. Kluwer Academic/Plenum Publishers, New York (2003)

    Book  Google Scholar 

  2. Chang, C.-I.: Hyperspectral Data Processing: Algorithm Design and Analysis. Wiley, Hoboken (2013)

    Book  MATH  Google Scholar 

  3. Reed, I.S., Yu, X.: Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans. Acoust. Speech Signal Process. 38(10), 1760–1770 (1990)

    Article  Google Scholar 

  4. Taitano, Y.P., Geier, B.A., Bauer, K.W.: A locally adaptable iterative RX detector. J. Adv. Signal Process. 2010(1), 1–10 (2010)

    Google Scholar 

  5. Matteoli, S., Diani, M., Corsini, G.: A kurtosis-based test to efficiently detect targets placed in close proximity by means of local covariance-based hyperspectral anomaly detectors. In: Proceedings of the 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Lisbon, Portugal (2011)

  6. Molero, J.M., Garzon, E.M., Garcia, I., Plaza, A.: Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 6(2), 801–814 (2013)

    Article  Google Scholar 

  7. Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 2nd edn. Academic, New York (1997)

    Google Scholar 

  8. Jensen, J.R.: Introductory Digital Image Processing: A Remote Sensing Perspective, 3rd edn. Prentice-Hall, Englewood Cliffs (2004)

    Google Scholar 

  9. Du, Q., Nekovei, R.: Fast real-time onboard processing of hyperspectral imagery for detection and classification. J. Real-Time Image Proc. 4(3), 273–286 (2009)

    Article  Google Scholar 

  10. Stellman, C.M., Hazel, G.G., Bucholtz, F., et al.: Real-time hyperspectral detection and cuing. Opt. Eng. 39(7), 1928–1935 (2000)

    Article  Google Scholar 

  11. Stevenson, B., O’Connor, R., Kendall, W., et al.: Design and performance of the Civil Air Patrol ARCHER hyperspectral processing system. Proc. SPIE. (2005)

  12. Tarabalka, Y., Haavardsholm, T.V., Kåsen, I., et al.: Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing. J. Real-Time Image Proc. 4(3), 287–300 (2009)

    Article  Google Scholar 

  13. Paz, A., Plaza, A.: GPU implementation of target and anomaly detection algorithms for remotely sensed hyperspectral image analysis. Proc Spie. 7810(2) (2010)

  14. Molero, J.M., Garzon, E.M., Garcia, I., et al.: Efficient implementation of hyperspectral anomaly detection techniques on GPUs and multicore processors. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 7(6), 2256–2266 (2014)

    Article  Google Scholar 

  15. Ensafi, E., Stocker, A.D.: An adaptive CFAR algorithm for real-time hyperspectral target detection. Proc SPIE (2008)

  16. Chen, S.Y., Wang, Y., Wu, C.C., et al.: Real-time causal processing of anomaly detection for hyperspectral imagery. IEEE Trans. Aerosp. Electron. Syst. 50(2), 1511–1534 (2014)

    Article  Google Scholar 

  17. Zhao, C., Wang, Y., Qi, B., et al.: Global and local real-time anomaly detectors for hyperspectral remote sensing imagery. Remote Sens. 7(4), 3966–3985 (2015)

    Article  Google Scholar 

  18. Wang, Y., Zhao, C.H., Chang, C.-I: Anomaly detection using sliding causal windows. In: Geoscience and Remote Sensing Symposium (IGARSS), Quebec, Canada. pp. 4600–4603 (2014)

  19. Chang, C.-I., Li, H.C., Song, M., et al.: Real-time constrained energy minimization for subpixel detection. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(6), 2545–2559 (2015)

    Article  Google Scholar 

  20. Li, H.C., Chang, C.-I., Song, M.: Recursive band processing of orthogonal subspace projection for hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 13(1), 3–7 (2016)

    Article  Google Scholar 

  21. Chang, C.-I., Li, Y.: Recursive band processing of automatic target generation process for finding unsupervised targets in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 54(9), 5081–5094 (2016)

    Article  Google Scholar 

  22. Peng B., Zhang L., Wu T., et al.: Fast real-time target detection via target-oriented band selection. In: IEEE Geoscience and Remote Sensing Symposium. (2016)

  23. Zhang, L., Peng, B., Zhang, F., et al.: Fast real-time causal linewise progressive hyperspectral anomaly detection via cholesky decomposition. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 10, 1–16 (2017)

    Article  Google Scholar 

  24. Acito, N., Matteoli, S., Diani, M., et al.: Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images. J. Real-Time Image Proc. 8(1), 53–86 (2013)

    Article  Google Scholar 

  25. Rossi, A., Acito, N., Diani, M., et al.: RX architectures for real-time anomaly detection in hyperspectral images. J. Real-Time Image Proc. 9(3), 503–517 (2014)

    Article  Google Scholar 

  26. Kailath, T.: Linear Systems. Prentice-Hall, Englewood Cliffs (1980)

    MATH  Google Scholar 

  27. Tapia, R.A., Lanius, C.: Computational science: tools for a changing world. Rice University (2001), [http://ceee.rice.edu/Books/CS/index.html]

  28. Harsanyi, J.C., Chang, C.-I.: Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach. IEEE Trans. Geosci. Remote Sens. 32(4), 779–785 (1994)

    Article  Google Scholar 

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Acknowledgements

This study is partially supported by the National Natural Science Foundation of China (No. 61571145), the China Postdoctoral Science Foundation (Grant No. 2014M551221), the Key Program of Heilongjiang Natural Science Foundation (No. ZD201216) and the Program Excellent Academic Leaders of Harbin (No. RC2013XK009003).

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Harbin Engineering University, Nantong Street, Harbin, China

    Chunhui Zhao

  2. Harbin Engineering University, Harbin, China

    Xifeng Yao

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  1. Chunhui Zhao
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  2. Xifeng Yao
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Corresponding author

Correspondence to Chunhui Zhao.

Appendix

Appendix

In this appendix, a desirable version of the global causal covariance matrix is derived.

$$ \begin{aligned} {\mathbf{C}}({\mathbf{L}}\text{(}n\text{)}) & = {\mathbf{R}}({\mathbf{L}}\text{(}n\text{)}) - {\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)}){\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)})^{T} \\ & = \frac{1}{n \times N}\sum\nolimits_{l = 1,i = 1}^{n,N} {{\mathbf{r}}_{li} } {\mathbf{r}}_{li}^{T} - {\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)}){\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)})^{T} \\ & = \frac{1}{n \times N}\left( {\sum\nolimits_{l = 1,i = 1}^{n - 1,N} {{\mathbf{r}}_{li} } {\mathbf{r}}_{li}^{T} + \sum\nolimits_{l = n - 1,i = 1}^{n,N} {{\mathbf{r}}_{li} } {\mathbf{r}}_{li}^{T} } \right) - {\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)}){\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)})^{T} \\ & = \frac{n - 1}{n}{\mathbf{R}}({\mathbf{L}}\text{(}n - 1\text{)}) + \frac{1}{n}{\mathbf{R}}({\mathbf{L}}_{n} ) - {\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)}){\varvec{\upmu}}({\mathbf{L}}\text{(}n\text{)})^{T} \\ \end{aligned} $$
(21)

Through Eq. (10), equation is rewritten as

$$ \begin{aligned} {\mathbf{C}}({\mathbf{L}}\text{(}n\text{)}) & = \frac{n - 1}{n}{\mathbf{R}}({\mathbf{L}}\text{(}n - 1\text{)}) + \frac{1}{n}{\mathbf{R}}({\mathbf{L}}_{n} ) \\ & \quad - \left( {\frac{n - 1}{n}{\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)}) + \frac{1}{n}{\varvec{\upmu}}({\mathbf{L}}_{n} )} \right)\left( {\frac{n - 1}{n}{\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)}) + \frac{1}{n}{\varvec{\upmu}}({\mathbf{L}}_{n} )} \right)^{T} \\ & = \frac{n - 1}{n}\left( {{\mathbf{R}}({\mathbf{L}}\text{(}n - 1\text{)}) - \frac{n - 1}{n}{\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)}){\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)})^{T} } \right) \\ & \quad + \frac{1}{n}\left( {{\mathbf{R}}({\mathbf{L}}_{n} ) - \frac{1}{n}{\varvec{\upmu}}({\mathbf{L}}_{n} ){\varvec{\upmu}}({\mathbf{L}}_{n} )^{T} } \right) - \frac{n - 1}{{n^{2} }}{\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)}){\varvec{\upmu}}({\mathbf{L}}_{n} )^{T} \\ & \quad - \frac{n - 1}{{n^{2} }}{\varvec{\upmu}}({\mathbf{L}}_{n} ){\varvec{\upmu}}({\mathbf{L}}\text{(}n - 1\text{)})^{T} \\ = & \frac{n - 1}{n}{\mathbf{C}}({\mathbf{L}}(n - 1)) + \frac{1}{n}{\mathbf{C}}({\mathbf{L}}_{n} ) \\ & \quad + \frac{n - 1}{{n^{2} }}[({\varvec{\upmu}}({\mathbf{L}}(n - 1)) - {\varvec{\upmu}}({\mathbf{L}}_{n} ))({\varvec{\upmu}}({\mathbf{L}}(n - 1)) - {\varvec{\upmu}}({\mathbf{L}}_{n} ))^{T} ] \\ \end{aligned}. $$
(22)

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Zhao, C., Yao, X. Progressive line processing of global and local real-time anomaly detection in hyperspectral images. J Real-Time Image Proc 16, 2289–2303 (2019). https://doi.org/10.1007/s11554-017-0738-8

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