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/ .
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    [CrossRef]
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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]

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