Abstract

This paper deals with point target detection in nonstationary backgrounds such as cloud scenes in aerial or satellite imaging. We propose an original spatial detection method based on first- and second-order modeling (i.e., mean and covariance) of local background statistics. We first show that state-of-the-art nonlocal denoising methods can be adapted with minimal effort to yield edge-preserving background mean estimates. These mean estimates lead to very efficient background suppression (BS) detection. However, we propose that BS be followed by a matched filter based on an estimate of the local spatial covariance matrix. The identification of these matrices derives from a robust classification of pixels in classes with homogeneous second-order statistics based on a Gaussian mixture model. The efficiency of the proposed approaches is demonstrated by evaluation on two cloudy sky background databases.

© 2012 Optical Society of America

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  1. I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
    [CrossRef]
  2. N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
    [CrossRef]
  3. L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.
  4. T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
    [CrossRef]
  5. S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
    [CrossRef]
  6. C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
    [CrossRef]
  7. J. Chen and I. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerospace Electron. Syst. 23, 46–59 (1987).
    [CrossRef]
  8. V. Samson, F. Champagnat, and J. Giovannelli, “Point target detection and subpixel position estimation in optical imagery,” Appl. Opt. 43, 257–263 (2004).
    [CrossRef]
  9. H. Van Trees, Detection, Estimation, and Modulation Theory: Detection, Estimation, and Linear Modulation Theory (Wiley, 1968).
  10. J. Barnett, “Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds,” Proc. SPIE 1050, 10–18 (1989).
  11. V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
    [CrossRef]
  12. X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
    [CrossRef]
  13. S. Kim, “Min-local-log filter for detecting small targets in cluttered background,” Electron. Lett. 47, 105–106 (2011).
    [CrossRef]
  14. E. Vasquez, F. Galland, G. Delyon, and P. Réfrégier, “Mixed segmentation-detection-based technique for point target detection in nonhomogeneous sky,” Appl. Opt. 49, 1518–1527 (2010).
    [CrossRef]
  15. C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 1998), pp. 839–846.
  16. A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.
  17. J. Pei, Z. Lu, and W. Xie, “A method for ir point target detection based on spatial-temporal bilateral filter,” in Proceedings of IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 846–849.
  18. J. Goudou, “Apport de la dimension temporelle aux traitements de veille infrarouge marine,” Ph.D. thesis (Telecom Paris, 2007). In French.
  19. K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
    [CrossRef]
  20. C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
    [CrossRef]
  21. A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
    [CrossRef]
  22. N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
    [CrossRef]
  23. C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
    [CrossRef]
  24. G. Celeux and G. Govaert, “A classification EM algorithm for clustering and two stochastic versions,” Comput. Statist. Data Anal. 14, 315–332 (1992).
    [CrossRef]
  25. T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd ed. (Wiley, 1984).
  26. G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).
  27. J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (University of California, 1967), pp. 281–297.
  28. http://www.cs.tut.fi/foi/GCF-BM3D/ .
  29. M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996).
    [CrossRef]
  30. https://eoportal.eumetsat.int/userMgmt/protected/dataCentre.faces .
  31. Y. Govaerts, “Eumetsat mission status, fire products/fire requirements,” slides presented at 2nd Workshop on Geostationary Fire Monitoring and Applications, Darmstadt, Germany, 4–6 December 2006, http://gofc-fire.umd.edu/products/pdfs/Events/Geo_2006/Govaerts_GOFC(1).pdf .

2012 (1)

G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).

2011 (2)

S. Kim, “Min-local-log filter for detecting small targets in cluttered background,” Electron. Lett. 47, 105–106 (2011).
[CrossRef]

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

2010 (1)

2009 (1)

X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
[CrossRef]

2008 (1)

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

2007 (2)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

2005 (1)

N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
[CrossRef]

2004 (1)

1999 (1)

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

1998 (1)

C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
[CrossRef]

1996 (1)

M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996).
[CrossRef]

1993 (2)

T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[CrossRef]

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

1992 (1)

G. Celeux and G. Govaert, “A classification EM algorithm for clustering and two stochastic versions,” Comput. Statist. Data Anal. 14, 315–332 (1992).
[CrossRef]

1989 (1)

J. Barnett, “Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds,” Proc. SPIE 1050, 10–18 (1989).

1987 (1)

J. Chen and I. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerospace Electron. Syst. 23, 46–59 (1987).
[CrossRef]

1985 (1)

A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
[CrossRef]

1983 (1)

I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
[CrossRef]

Acito, N.

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
[CrossRef]

Anderson, T. W.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd ed. (Wiley, 1984).

Antonelli, D.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

Attili, J. B.

C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
[CrossRef]

Bai, X.

X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
[CrossRef]

Barnett, J.

J. Barnett, “Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds,” Proc. SPIE 1050, 10–18 (1989).

Bar-Tal, M.

M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996).
[CrossRef]

Bondaryk, J. E.

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

Buades, A.

A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Caefer, C. E.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Celeux, G.

G. Celeux and G. Govaert, “A classification EM algorithm for clustering and two stochastic versions,” Comput. Statist. Data Anal. 14, 315–332 (1992).
[CrossRef]

Champagnat, F.

V. Samson, F. Champagnat, and J. Giovannelli, “Point target detection and subpixel position estimation in optical imagery,” Appl. Opt. 43, 257–263 (2004).
[CrossRef]

L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.

Chan, C. L.

C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
[CrossRef]

Chan, P.

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

Chen, J.

J. Chen and I. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerospace Electron. Syst. 23, 46–59 (1987).
[CrossRef]

Coll, B.

A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Coret, L.

L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.

Corsini, G.

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
[CrossRef]

Dabov, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Delyon, G.

Deshpande, S. D.

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

Diani, M.

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
[CrossRef]

Egiazarian, K.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Er, M. H.

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

Foi, A.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Gagliardi, R.

A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
[CrossRef]

I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
[CrossRef]

Galland, F.

Genin, L.

L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.

Giovannelli, J.

Goudou, J.

J. Goudou, “Apport de la dimension temporelle aux traitements de veille infrarouge marine,” Ph.D. thesis (Telecom Paris, 2007). In French.

Govaert, G.

G. Celeux and G. Govaert, “A classification EM algorithm for clustering and two stochastic versions,” Comput. Statist. Data Anal. 14, 315–332 (1992).
[CrossRef]

Govaerts, Y.

Y. Govaerts, “Eumetsat mission status, fire products/fire requirements,” slides presented at 2nd Workshop on Geostationary Fire Monitoring and Applications, Darmstadt, Germany, 4–6 December 2006, http://gofc-fire.umd.edu/products/pdfs/Events/Geo_2006/Govaerts_GOFC(1).pdf .

Jin, T.

X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
[CrossRef]

Katkovnik, V.

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

Kim, S.

S. Kim, “Min-local-log filter for detecting small targets in cluttered background,” Electron. Lett. 47, 105–106 (2011).
[CrossRef]

Ku, W.

T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[CrossRef]

Le Besnerais, G.

L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.

Leung, M.

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

Lu, Z.

J. Pei, Z. Lu, and W. Xie, “A method for ir point target detection based on spatial-temporal bilateral filter,” in Proceedings of IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 846–849.

MacQueen, J.

J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (University of California, 1967), pp. 281–297.

Mallat, S.

G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).

Manduchi, R.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 1998), pp. 839–846.

Margalit, A.

A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
[CrossRef]

Melendez, K. A.

C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
[CrossRef]

Morel, J.

A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

Nielsen, E. D.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Orthal, O.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

Pei, J.

J. Pei, Z. Lu, and W. Xie, “A method for ir point target detection based on spatial-temporal bilateral filter,” in Proceedings of IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 846–849.

Peli, T.

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

Raviv, O.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Reed, I.

J. Chen and I. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerospace Electron. Syst. 23, 46–59 (1987).
[CrossRef]

A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
[CrossRef]

I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
[CrossRef]

Réfrégier, P.

Rizzuto, A. P.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Rossi, A.

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

Rotman, S. R.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996).
[CrossRef]

Samson, V.

Sapiro, G.

G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).

Shao, H.

I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
[CrossRef]

Sharoni, Y.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

Silverman, J.

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

Soni, T.

T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[CrossRef]

Stefanou, M. S.

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Tom, V. T.

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

Tomasi, C.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 1998), pp. 839–846.

Van Trees, H.

H. Van Trees, Detection, Estimation, and Modulation Theory: Detection, Estimation, and Linear Modulation Theory (Wiley, 1968).

Vasquez, E.

Venkateswarlu, R.

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

Xie, W.

J. Pei, Z. Lu, and W. Xie, “A method for ir point target detection based on spatial-temporal bilateral filter,” in Proceedings of IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 846–849.

Yu, G.

G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).

Zeidler, J.

T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[CrossRef]

Zhou, F.

X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
[CrossRef]

Appl. Opt. (2)

Comput. Statist. Data Anal. (1)

G. Celeux and G. Govaert, “A classification EM algorithm for clustering and two stochastic versions,” Comput. Statist. Data Anal. 14, 315–332 (1992).
[CrossRef]

Electron. Lett. (1)

S. Kim, “Min-local-log filter for detecting small targets in cluttered background,” Electron. Lett. 47, 105–106 (2011).
[CrossRef]

IEEE Trans. Aerospace Electron. Syst. (3)

J. Chen and I. Reed, “A detection algorithm for optical targets in clutter,” IEEE Trans. Aerospace Electron. Syst. 23, 46–59 (1987).
[CrossRef]

I. Reed, R. Gagliardi, and H. Shao, “Application of three-dimensional filtering to moving target detection,” IEEE Trans. Aerospace Electron. Syst. 19, 898–905 (1983).
[CrossRef]

A. Margalit, I. Reed, and R. Gagliardi, “Adaptive optical target detection using correlated images,” IEEE Trans. Aerospace Electron. Syst. 21, 394–405 (1985).
[CrossRef]

IEEE Trans. Image Process. (3)

K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, “Image denoising by sparse 3-D transform-domain collaborative filtering,” IEEE Trans. Image Process. 16, 2080–2095 (2007).
[CrossRef]

G. Yu, G. Sapiro, and S. Mallat, “Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity,” IEEE Trans. Image Process. 21, 2481–2499 (2012).

T. Soni, J. Zeidler, and W. Ku, “Performance evaluation of 2-d adaptive prediction filters for detection of small objects in image data,” IEEE Trans. Image Process. 2, 327–340 (1993).
[CrossRef]

Infrared Phys. Technol. (1)

M. Bar-Tal and S. R. Rotman, “Performance measurement in point source target detection,” Infrared Phys. Technol. 37, 231–238 (1996).
[CrossRef]

Opt. Eng. (3)

N. Acito, A. Rossi, M. Diani, and G. Corsini, “Optimal criterion to select the background estimation algorithm for detection of dim point targets in infrared surveillance systems,” Opt. Eng. 50, 107204 (2011).
[CrossRef]

C. E. Caefer, J. Silverman, O. Orthal, D. Antonelli, Y. Sharoni, and S. R. Rotman, “Improved covariance matrices for point target detection in hyperspectral data,” Opt. Eng. 47, 076402 (2008).
[CrossRef]

C. E. Caefer, M. S. Stefanou, E. D. Nielsen, A. P. Rizzuto, O. Raviv, and S. R. Rotman, “Analysis of false alarm distributions in the development and evaluation of hyperspectral point target detection algorithms,” Opt. Eng. 46, 076402 (2007).
[CrossRef]

Proc. SPIE (5)

J. Barnett, “Statistical analysis of median subtraction filtering with application to point target detection in infrared backgrounds,” Proc. SPIE 1050, 10–18 (1989).

V. T. Tom, T. Peli, M. Leung, and J. E. Bondaryk, “Morphology-based algorithm for point target detection in infrared backgrounds,” Proc. SPIE 1954, 2–11 (1993).
[CrossRef]

S. D. Deshpande, M. H. Er, R. Venkateswarlu, and P. Chan, “Max-mean and max-median filters for detection of small targets,” Proc. SPIE 3809, 74–83 (1999).
[CrossRef]

N. Acito, M. Diani, and G. Corsini, “Gaussian mixture model based approach to anomaly detection in multi/hyperspectral images,” Proc. SPIE 5982, 59820O (2005).
[CrossRef]

C. L. Chan, J. B. Attili, and K. A. Melendez, “Image segmentation approach for improving target detection in a 3D signal processor,” Proc. SPIE 3373, 87–94 (1998).
[CrossRef]

Signal Process. (1)

X. Bai, F. Zhou, and T. Jin, “Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter,” Signal Process. 90, 1643–1654 (2009).
[CrossRef]

Other (11)

H. Van Trees, Detection, Estimation, and Modulation Theory: Detection, Estimation, and Linear Modulation Theory (Wiley, 1968).

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 1998), pp. 839–846.

A. Buades, B. Coll, and J. Morel, “A non-local algorithm for image denoising,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 60–65.

J. Pei, Z. Lu, and W. Xie, “A method for ir point target detection based on spatial-temporal bilateral filter,” in Proceedings of IEEE International Conference on Pattern Recognition (IEEE, 2006), pp. 846–849.

J. Goudou, “Apport de la dimension temporelle aux traitements de veille infrarouge marine,” Ph.D. thesis (Telecom Paris, 2007). In French.

L. Genin, F. Champagnat, G. Le Besnerais, and L. Coret, “Point object detection using a NL-means type filter,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2011), pp. 3533–3536.

https://eoportal.eumetsat.int/userMgmt/protected/dataCentre.faces .

Y. Govaerts, “Eumetsat mission status, fire products/fire requirements,” slides presented at 2nd Workshop on Geostationary Fire Monitoring and Applications, Darmstadt, Germany, 4–6 December 2006, http://gofc-fire.umd.edu/products/pdfs/Events/Geo_2006/Govaerts_GOFC(1).pdf .

J. MacQueen, “Some methods for classification and analysis of multivariate observations,” in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability (University of California, 1967), pp. 281–297.

http://www.cs.tut.fi/foi/GCF-BM3D/ .

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, 2nd ed. (Wiley, 1984).

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Figures (11)

Fig. 1.
Fig. 1.

General principle of detection algorithms based on the GLRT.

Fig. 2.
Fig. 2.

Illustration of the effect of the two steps of the detection principle derived from GLRT (Fig. 1). (a) Input image presenting strong transition areas (roof edges) and textured areas (bricks). (b) Zoom on a part of the image. (c) Zoom on the residual obtained by subtracting, from each pixel of the image, the mean estimated by an average of the neighborhood pixels in a 3×3 pixels window. (d) Zoom on the residual obtained after the application of a MF, on the residual obtained in (c) with a covariance matrix estimated from all the pixels of the image.

Fig. 3.
Fig. 3.

Illustration of the block-matching step of the BM3D, which builds groups by associating to each reference patch (thick borders) the patches that are similar.

Fig. 4.
Fig. 4.

Zoom on the residual obtained for the image in the Fig. 2(a), after the application of various background-suppression methods: (a) based on mean filter, (b) based on max-median filter, (c) top-bottom hat algorithm, (d) DBM3D algorithm.

Fig. 5.
Fig. 5.

Illustration of the robustness of the classification to a target presence. (a) Image of a cloudy sky with an embedded target at the center of the circle and a zoom on the target position (the target position is pointed by the arrow). (b) Residual obtained after BS by BM3D and a zoom on the target position. (c) Classification obtained after nine iterations for N=9 and K=7 on the residual image (b). (d) Zoom on the segmentation around the target.

Fig. 6.
Fig. 6.

Ground database.

Fig. 7.
Fig. 7.

Satellite database.

Fig. 8.
Fig. 8.

Object signature for different subpixel positions (ϵi,ϵj), from left to right (0,0), (0,0.4), and (0.4,0.4), and intensity of the central pixel.

Fig. 9.
Fig. 9.

ROC curves showing performances of point target detection methods by BS using various background first-order modeling techniques.

Fig. 10.
Fig. 10.

ROC curves showing performances of point target detection methods based on background second-order modeling with a comparison to the DBM3D ones.

Fig. 11.
Fig. 11.

Top line: residual obtained for one image of the ground database without target, after the application of various detection methods—residual normalized by the threshold associated to a FA rate of 102. Second line: positions of the FA and associated criterion DNFA. Third line: potential target detection image—same normalization as the one applied for the residuals of the first line. Bottom line: positions of the nondetections.

Tables (3)

Tables Icon

Table 1. DNFA Criterion for the Ground Database

Tables Icon

Table 2. DNFA Criterion for the Satellite Database

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Table 3. DNFA Criterion for Uniformly Distributed Pseudorandom Numbers

Equations (18)

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{H0:ux=bx(background only)H1:ux=bx+αs(present target),
p(ux|H1)p(ux|H0)H0H1T1,
maxαp(ux|H1,α)p(ux|H0)H0H1T1.
sTΣx1(uxmx)[sTΣx1s]1/2H0H12log(T1).
d2(ux,uxr)=uxuxr22(N1)2,
p(rx|Σkx)=1(2π)N2/2|Σkx|1/2exp(12rxTΣkx1rx),
k^x=argmin1kKlogp(rx|Σ^k)=argmin1kK(log|Σ^k|+rxTΣ^k1rx).
Σ^k=argmaxΣp({rx}xCk|Σ)=argmaxΣxCkp(rx|Σ),1kK.
Σ^k=1Card(Ck)xCkrxrxT,1kK,
sTΣ^k^x1rx[sTΣ^k^x1s]1/2H0H12log(T1).
s(ϵi,ϵj)(x)=soi0.5i+0.5j0.5j+0.5ho(vϵi,wϵj)dvdw.
maxα0p(ux|H1,α)p(ux|H0)H0H1T1
sTΣx1(uxmx)[sTΣx1s]1/2H0H12logT1,
maxα0p(ux|H1,α)p(ux|H1,α=0)=p(ux|H0).
maxα02log(p(ux|H1,α)p(ux|H0))H0H12logT1.
P(α)=2log(p(ux|H1,α)p(ux|H0)).
P(α)=sTΣx1sα2+2sTΣx1(uxmx)α.
maxα0P(α)=[sTΣx1(uxmx)]2sTΣx1s.

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