Abstract

Based on volume holographic correlator, a multi-sample parallel estimation method is proposed to implement remote sensing image recognition with high accuracy. The essential steps of the method including image preprocessing, estimation curves fitting, template images preparation and estimation equation establishing are discussed in detail. The experimental results show the validity of the multi-sample parallel estimation method, and the recognition accuracy is improved by increasing the sample numbers.

© 2009 OSA

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  1. G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
    [CrossRef]
  2. Y. Takashima and L. Hesselink, “Media tilt tolerance of bit-based and page-based holographic storage systems,” Opt. Lett. 31(10), 1513–1515 (2006).
    [CrossRef] [PubMed]
  3. G. W. Burr, S. Kobras, H. Hanssen, and H. Coufal, “Content-addressable data storage by use of volume holograms,” Appl. Opt. 38(32), 6779–6784 (1999).
    [CrossRef] [PubMed]
  4. B. J. Goertzen and P. A. Mitkas, “Volume holographic storage for large relational databases,” Opt. Eng. 35(7), 1847–1853 (1996).
    [CrossRef]
  5. L. Hesselink, S. S. Orlov, and M. C. Bashaw, “Holographic Data Storage Systems,” in Proceedings of IEEE Conference on Digital Object Identifier (Institute of Electrical and Electronics Engineers, New York, 2004), pp. 1231–1280.
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    [CrossRef]
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    [CrossRef]
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    [CrossRef] [PubMed]
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    [CrossRef]
  13. “S. Fumihiko, “Image template matching based on edge-spin correlation,” Electr. Eng. 153, 1592–1596 (2005).
  14. S. D. Wei and S. H. Lai, “Robust and efficient image alignment based on relative gradient matching,” IEEE Trans. Image Process. 15(10), 2936–2943 (2006).
    [CrossRef] [PubMed]
  15. T. S. Huang, “PCM Picture Transmission,” IEEE Spectr. 2, 57–63 (1965).
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  18. P. M. Lundquist, C. Poga, R. G. Devoe, Y. Jia, W. E. Moerner, M.-P. Bernal, H. Coufal, R. K. Grygier, J. A. Hoffnagle, C. M. Jefferson, R. M. Macfarlane, R. M. Shelby, and G. T. Sincerbox, “Holographic digital data storage in a photorefractive polymer,” Opt. Lett. 21(12), 890–892 (1996).
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    [CrossRef] [PubMed]
  20. R. V. Hogg, and A. T. Craig, Introduction to Mathematical Statistics (The Macmillan Company, 1959).
  21. A. Baraldi and F. Paramiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. Geosci. Rem. Sens. 3, 293–304 (1993).
  22. C. Rafael, Gonzalez, Digital image processing (New York, 2005).
  23. R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
    [CrossRef]

2008 (1)

2007 (2)

K. Ni, Z. Y. Qu, L. C. Cao, P. Su, Q. S. He, and G. F. Jin, “High accurate volume holographic correlator with 4000 parallel correlation channels,” Proc. SPIE 6827, 6827J (2007).

K. Ni, Z. Y. Qu, L. C. Cao, P. Su, Q. S. He, and G. F. Jin, “Improving accuracy of multichannel volume holographic correlators by using a two-dimensional interleaving method,” Opt. Lett. 32(20), 2972–2975 (2007).
[CrossRef] [PubMed]

2006 (3)

Y. Takashima and L. Hesselink, “Media tilt tolerance of bit-based and page-based holographic storage systems,” Opt. Lett. 31(10), 1513–1515 (2006).
[CrossRef] [PubMed]

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

S. D. Wei and S. H. Lai, “Robust and efficient image alignment based on relative gradient matching,” IEEE Trans. Image Process. 15(10), 2936–2943 (2006).
[CrossRef] [PubMed]

2005 (1)

“S. Fumihiko, “Image template matching based on edge-spin correlation,” Electr. Eng. 153, 1592–1596 (2005).

2003 (1)

1999 (1)

1997 (1)

A. Pu, R. Denkewalter, and D. Psaltis, “Real-time vehicle navigation using a holographic memory,” Opt. Eng. 36(10), 2737–2746 (1997).
[CrossRef]

1996 (3)

1994 (1)

G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
[CrossRef]

1993 (1)

A. Baraldi and F. Paramiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. Geosci. Rem. Sens. 3, 293–304 (1993).

1973 (1)

R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[CrossRef]

1966 (1)

L. E. Franks, “A Mode for the Random Video Process,” Bell Syst. Tech. J. 45, 609–630 (1966).

1965 (1)

T. S. Huang, “PCM Picture Transmission,” IEEE Spectr. 2, 57–63 (1965).

1959 (1)

J. Capon, “A Probabilistic Mode for Run Length Coding of Picture,” IEEE Trans. Inf. Theory 5(4), 157–163 (1959).
[CrossRef]

Baraldi, A.

A. Baraldi and F. Paramiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. Geosci. Rem. Sens. 3, 293–304 (1993).

Bernal, M.-P.

Burr, G. W.

G. W. Burr, S. Kobras, H. Hanssen, and H. Coufal, “Content-addressable data storage by use of volume holograms,” Appl. Opt. 38(32), 6779–6784 (1999).
[CrossRef] [PubMed]

G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
[CrossRef]

Cao, L. C.

Capon, J.

J. Capon, “A Probabilistic Mode for Run Length Coding of Picture,” IEEE Trans. Inf. Theory 5(4), 157–163 (1959).
[CrossRef]

Coufal, H.

Denkewalter, R.

A. Pu, R. Denkewalter, and D. Psaltis, “Real-time vehicle navigation using a holographic memory,” Opt. Eng. 36(10), 2737–2746 (1997).
[CrossRef]

Devoe, R. G.

Dinstein, I. H.

R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[CrossRef]

Franks, L. E.

L. E. Franks, “A Mode for the Random Video Process,” Bell Syst. Tech. J. 45, 609–630 (1966).

Fumihiko, S.

“S. Fumihiko, “Image template matching based on edge-spin correlation,” Electr. Eng. 153, 1592–1596 (2005).

Goertzen, B. J.

B. J. Goertzen and P. A. Mitkas, “Volume holographic storage for large relational databases,” Opt. Eng. 35(7), 1847–1853 (1996).
[CrossRef]

Grygiel, R. K.

Grygier, R. K.

Hanssen, H.

Haralick, R. M.

R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[CrossRef]

He, Q. S.

Heifetz, A.

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

Hesselink, L.

Hoffnagle, J. A.

Huang, T. S.

T. S. Huang, “PCM Picture Transmission,” IEEE Spectr. 2, 57–63 (1965).

Jefferson, C. M.

Jia, Y.

Jin, G. F.

Kobras, S.

Lai, S. H.

S. D. Wei and S. H. Lai, “Robust and efficient image alignment based on relative gradient matching,” IEEE Trans. Image Process. 15(10), 2936–2943 (2006).
[CrossRef] [PubMed]

Lee, J. K.

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

Liao, Y.

Lundquist, P. M.

Macfarlane, R. M.

Mitkas, P. A.

B. J. Goertzen and P. A. Mitkas, “Volume holographic storage for large relational databases,” Opt. Eng. 35(7), 1847–1853 (1996).
[CrossRef]

Moerner, W. E.

Mok, F. H.

G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
[CrossRef]

Ni, K.

Ouyang, C.

Paramiggiani, F.

A. Baraldi and F. Paramiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. Geosci. Rem. Sens. 3, 293–304 (1993).

Poga, C.

Psaltis, D.

A. Pu, R. Denkewalter, and D. Psaltis, “Real-time vehicle navigation using a holographic memory,” Opt. Eng. 36(10), 2737–2746 (1997).
[CrossRef]

G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
[CrossRef]

Pu, A.

A. Pu, R. Denkewalter, and D. Psaltis, “Real-time vehicle navigation using a holographic memory,” Opt. Eng. 36(10), 2737–2746 (1997).
[CrossRef]

Qu, Z. Y.

Ren, W.

Shahriar, M. S.

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

Shanmugan, K.

R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[CrossRef]

Shelby, R. M.

Shen, J. T.

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

Sincerbox, G. T.

Su, P.

K. Ni, Z. Y. Qu, L. C. Cao, P. Su, Q. S. He, and G. F. Jin, “Improving accuracy of multichannel volume holographic correlators by using a two-dimensional interleaving method,” Opt. Lett. 32(20), 2972–2975 (2007).
[CrossRef] [PubMed]

K. Ni, Z. Y. Qu, L. C. Cao, P. Su, Q. S. He, and G. F. Jin, “High accurate volume holographic correlator with 4000 parallel correlation channels,” Proc. SPIE 6827, 6827J (2007).

Takashima, Y.

Tripathi, R.

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

Wei, S. D.

S. D. Wei and S. H. Lai, “Robust and efficient image alignment based on relative gradient matching,” IEEE Trans. Image Process. 15(10), 2936–2943 (2006).
[CrossRef] [PubMed]

Wimmer, P.

Wittmann, G.

Wu, M. X.

Appl. Opt. (2)

Bell Syst. Tech. J. (1)

L. E. Franks, “A Mode for the Random Video Process,” Bell Syst. Tech. J. 45, 609–630 (1966).

Electr. Eng. (1)

“S. Fumihiko, “Image template matching based on edge-spin correlation,” Electr. Eng. 153, 1592–1596 (2005).

IEEE Spectr. (1)

T. S. Huang, “PCM Picture Transmission,” IEEE Spectr. 2, 57–63 (1965).

IEEE Trans. Geosci. Rem. Sens. (1)

A. Baraldi and F. Paramiggiani, “An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters,” IEEE Trans. Geosci. Rem. Sens. 3, 293–304 (1993).

IEEE Trans. Image Process. (1)

S. D. Wei and S. H. Lai, “Robust and efficient image alignment based on relative gradient matching,” IEEE Trans. Image Process. 15(10), 2936–2943 (2006).
[CrossRef] [PubMed]

IEEE Trans. Inf. Theory (1)

J. Capon, “A Probabilistic Mode for Run Length Coding of Picture,” IEEE Trans. Inf. Theory 5(4), 157–163 (1959).
[CrossRef]

IEEE Trans. Syst. Man Cybern. (1)

R. M. Haralick, K. Shanmugan, and I. H. Dinstein, “Textural features for image classification,” IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973).
[CrossRef]

Opt. Eng. (3)

B. J. Goertzen and P. A. Mitkas, “Volume holographic storage for large relational databases,” Opt. Eng. 35(7), 1847–1853 (1996).
[CrossRef]

A. Heifetz, J. T. Shen, J. K. Lee, R. Tripathi, and M. S. Shahriar, “Translation-invariant object recognition system using an optical correlator and a superparallel holographic random access memory,” Opt. Eng. 45(2), 1–5 (2006).
[CrossRef]

A. Pu, R. Denkewalter, and D. Psaltis, “Real-time vehicle navigation using a holographic memory,” Opt. Eng. 36(10), 2737–2746 (1997).
[CrossRef]

Opt. Lett. (5)

Proc. SPIE (2)

K. Ni, Z. Y. Qu, L. C. Cao, P. Su, Q. S. He, and G. F. Jin, “High accurate volume holographic correlator with 4000 parallel correlation channels,” Proc. SPIE 6827, 6827J (2007).

G. W. Burr, F. H. Mok, and D. Psaltis, “Large-scale volume holographic storage in the long interaction length architecture,” Proc. SPIE 2297, 402–414 (1994).
[CrossRef]

Other (4)

L. Hesselink, S. S. Orlov, and M. C. Bashaw, “Holographic Data Storage Systems,” in Proceedings of IEEE Conference on Digital Object Identifier (Institute of Electrical and Electronics Engineers, New York, 2004), pp. 1231–1280.

C. Rafael, Gonzalez, Digital image processing (New York, 2005).

H. Andrew, Jazwinskl, Stochastic process and filtering theory (New York and London,1970).

R. V. Hogg, and A. T. Craig, Introduction to Mathematical Statistics (The Macmillan Company, 1959).

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Figures (12)

Fig. 1
Fig. 1

The schematic diagram of the segmentation of the remote sensing image.

Fig. 2
Fig. 2

A remote sensing image and its correlation function.

Fig. 3
Fig. 3

A typical correlation curve of remote sensing image without preprocessing.

Fig. 4
Fig. 4

Typical correlation curves of remote sensing image using different times of thinning algorithm, (a) once; (b) three times; (c)six times.

Fig. 5
Fig. 5

Remote sensing image after different times of thinning algorithm. (a) Without preprocessing; (b) once; (c) three times; (d)six times.

Fig. 6
Fig. 6

The schematic diagram of one-dimensional MPE of remote sensing image recognition.

Fig. 7
Fig. 7

The schematic diagram of estimation equation of correlation spots

Fig. 8
Fig. 8

Experiment setup for test the MPE method used in the VHC. PBS, polarizing beam splitter; SLM, spatial light modulator; S, shutter; L1, L2, L3 and L4 lenses; M, mirror; λ/2, half-wave-plate.

Fig. 9
Fig. 9

An actual example of remote sensing image recognition. (a) Reference image and a sample of the template image; (b) preprocessing of the reference image.

Fig. 10
Fig. 10

The experiment of the multiple sample estimation. (a) Detected correlation spots by inputting white image; (b) detected correlation spots by inputting the target image;(c) 16(4 × 4) sample spots to be used.

Fig. 11
Fig. 11

The error radius distribution with different sample number. (a) Only with the brightest spot; sample number is (b) 4(2 × 2); (c) 16(4 × 4) and (d) 36(6 × 6).

Fig. 12
Fig. 12

Curve of relationship between the accuracy and increasing number.

Tables (1)

Tables Icon

Table 1 The error radius with different sample number.

Equations (9)

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R ( Δ x , Δ y ) = E [ g ( x , y ) g ( x + Δ x , y + Δ y ) ]                 = a × exp ( α | Δ x | β | Δ y | ) + b ,
ρ = d x 2 + d y 2 ,
f n ( Δ x , Δ y ) = R 2 ( Δ x , Δ y ) = ( a × e α | Δ x | β | Δ y | + b ) 2 .
{ f n ( x + Δ ) = a 1 f n ( x ) = a 2 f n ( x Δ ) = a 3 ,
F ( x , y ) = { m 11 = f 11 ( x , y ) m 12 = f 12 ( x , y ) ... m u v = f u v ( x , y ) ... m p q = f p q ( x , y ) ,
F ( x , y ) = { m 11 = f u v ( x +( u 1 ) Δ , y + ( v 1 ) Δ ) m 12 = f u v ( x + ( u 2 ) Δ , y + ( v 1 ) Δ ) ... m u v = f u v ( x , y ) ... m p q = f u v ( x + ( u p ) Δ , y + ( v q ) Δ ) ,
[ 0.3212 0.5324 0.5429 0.0198 0.2135 0.6035 0.8789 0.3869 0.1206 0.7878 0.6057 0.4108 0.4293 0.6512 0.1213 0.3909 ] .
[ f 11 ( x , y ) = 0.3212 f 11 ( x 3 , y ) = 0.5324 f 11 ( x 6 , y ) = 0.5429 f 11 ( x 9 , y ) = 0.0198 f 11 ( x , y 3 ) = 0.2135 f 11 ( x 3 , y 3 ) = 0.6035 f 11 ( x 3 , y 3 ) = 0.8789 f 11 ( x 9 , y 3 ) = 0.3869 f 11 ( x , y 6 ) = 0.1206 f 11 ( x 3 , y 6 = 0.7878 f 11 ( x 6 , y 6 ) = 0.6057 f 11 ( x 9 , y 6 ) = 0.4108 f 11 ( x , y 9 ) = 0.4293 f 11 ( x 3 , y 6 ) = 0.6512 f 11 ( x 6 , y 6 ) = 0.1213 f 11 ( x 9 , y 9 ) = 0.3909 ] .
[ 0.4816 0.5971 0.2231 0.4541 0.5608 0.6329 0.6221 0.4645 0.7736 0.7071 0.8141 0.5524 0.3122 0.4902 0.5751 0.3389 ] .

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