Synthetic aperture radar image compression

• US 7,245,250 B1
• Filed: 08/16/2005
• Issued: 07/17/2007
• Est. Priority Date: 08/16/2005
• Status: Active Grant

• Current Assignee: Harris Corporation (L3Harris Technologies, Inc.)
• Original Assignee: ITT Manufacturing Enterprises, Inc. (ITT, Inc.)
• Inventors: Kalayeh, Hooshmand M.
• Primary Examiner(s): Sotomayor; John B.
• Application Number:
  US11/204,892
• Time in Patent Office: 700 Days
• Field of Search: 342/25.R, 342/25.A, 342/25.B, 342/25.C, 342/25.D, 342/25.E, 342/25.F, 342/89, 342 98-101, 342/189, 342/192, 342/196
• US Class Current: 342/25.00R
• CPC Class Codes: G01S 13/9011 with frequency domain processing of the SAR signals in azimuth G01S13/9005 takes precedence
• CPC Class Codes: G01S 13/9017 with time domain processing of the SAR signals in azimuth G01S13/9005 takes precedence
• CPC Class Codes: G01S 7/003 Transmission of data between radar, sonar or lidar systems and remote stations

Abstract

A method is disclosed of generating multi-resolution images using synthetic aperture radar (SAR) phase history data. A high resolution SAR image may be formed from the phase history data, or a lower resolution SAR image may be formed from a sub-aperture of the phase history data. The multi-resolution images may also be compressed. Compressed multi-resolution images are progressively transmitted to a client until the client receives an image with adequate image quality and/or resolution. Compressed multi-resolution images may also be used for iterative target detection, where images are analyzed by a target detection processor, starting with a lowest resolution image, going to a highest resolution image, until an adequate image of the target or scene is obtained. If a target is not found in the multi-resolution images, a new sub-aperture is chosen on the phase history data, and the target detection process repeats for the new sub-aperture.

Claims

1. A method of generating multi-resolution synthetic aperture radar (SAR) images, the method comprising the steps of:

a) obtaining SAR phase history data, denoted as PH;

b) calculating an inverse Fourier transform of a first data set, denoted as ph₀, of the SAR phase history data, PH, to form a highest resolution image, denoted as G₀, where

G₀=FT<sup>−

1</sup>(ph₀);

c) calculating the inverse Fourier transform of a plurality of other data sets, denoted as ph₁ . . . ph_N, to form a plurality of correlated lower resolution images, denoted as G₁ . . . G_N, where

G_i=FT<sup>−

1</sup>(ph_i), for i=1 to N;

d) subtracting successive ones of the highest resolution image, G₀, and the lower resolution images, G₁ . . . G_N, to obtain a plurality of edge enhanced images, denoted as L₀ . . . L<sub>N−

1</sub>, where

L_i=G_i−

G_i+1, for i=0 to N−

1; and

e) obtaining a lowest resolution image, denoted as L_N.

2. A method according to claim 1, wherein steps (b)–

(f) are repeated for a new first data set and a new plurality of other data sets, if one or more of the highest resolution image and the plurality of correlated lower resolution images have undesirable characteristics.

3. A method according to claim 1, further including the step of encoding the plurality of edge enhanced images and the lowest resolution image.

4. The method of claim 3, wherein the encoding step encodes according to one of a Huffman code, a range code, an arithmetic code, a Unary code, an Elias code, a Fibonacci code, a Golomb code, or a Rice code.

5. The method of claim 1, wherein the first data set, ph₀, comprises the SAR phase history data, PH:

ph₀=PH.

6. The method of claim 1, wherein the first data set, ph₀, comprises a first sub-aperture set, denoted as SA₀, of the SAR phase history data, PH, and step (b) further comprises multiplying the SAR phase history data, PH, by a function representing the first sub-aperture set, denoted as f_SA0, where

ph₀=PH·

f_SA0.

7. The method of claim 6, wherein the first sub-aperture set, SA₀, is chosen from one of a circularly symmetric Gaussian, a circularly asymmetric Gaussian, a rectangle, an annuli, or a sector.

8. A method according to claim 6, further comprising the step of successively multiplying the first data set, ph₀, by the function representing the first sub-aperture set, f_SA0, to obtain the plurality of other data sets, ph₁ . . . ph_N, where

ph_i=ph₀·

(f_SA0)ⁱ⁺¹, for i=1 to N.

9. The method of claim 1, wherein the plurality of other data sets, ph₁ . . . ph_N, comprise a respective plurality of other sub-aperture sets, denoted as SA₁ . . . SA_N, of the SAR phase history data, PH, and step (c) further comprises multiplying the SAR phase history data, PH, by a respective function representing each one of the plurality of other sub-aperture sets:

ph_i=PH·

f_SAi, for i=1 to N.

10. The method of claim 8, wherein the plurality of other sub-aperture sets, SA₁ . . . SA_N, are chosen from one or more of a circularly symmetric Gaussian, a circularly asymmetric Gaussian, a rectangle, an annuli, and a sector.

11. A method according to claim 1, further comprising the steps of:

b1) integrating over the first data set, ph₀, and each one of the plurality of other data sets, ph₁ . . . ph_N, to obtain a plurality of features, denoted as F₀ . . . F_N;

b2) storing the plurality of features, F₀ . . . F_N;

b3) performing target detection based on the stored plurality of features to obtain a receiver operating characteristic (ROC);

b4) determining if the ROC is acceptable; and

b5) repeating the method for a new first data set and a new plurality of other data sets if the ROC is unacceptable.

12. The method of claim 11, wherein step (b3) includes inputting the plurality of features to one or more of a statistical, maximum likelihood classifier, and a neural network, packpropagation classifier.

13. The method of claim 12, wherein step (b3) further includes inputting one or more prototype vectors to the classifier(s), each vector corresponding to a respective target or class.

14. The method of claim 11, wherein the first data set, ph₀, and the plurality of other data sets, ph₁ . . . ph_N, include a respective plurality of sub-aperture sets, denoted as SA₀ . . . SA_N, of the SAR phase history data, PH, and the method further includes the step of:

choosing the plurality of sub-aperture sets, SA₀ . . . SA_N, from one or more of a circularly symmetric Gaussian, a circularly asymmetric Gaussian, a rectangle, an annuli, and a sector.

15. A method of generating multi-resolution synthetic aperture radar (SAR) images, the method comprising the steps of:

a) obtaining SAR phase history data, denoted as PH;

b) computing an inverse Fourier transform of a first data set, denoted as ph₀, of the SAR phase history data, PH, to form a highest resolution image, denoted as G₀, where

G₀=FT<sup>−

1</sup> (ph₀);

c) successively low-pass filtering the highest resolution image, G₀, to form a plurality of correlated lower resolution images, denoted as G₁ . . . G_N, where

G_i+1=LPF(G_i), for i=0 to N−

1;

d) subtracting successive ones of the highest resolution image, G₀, and the lower resolution images, G₁ . . . G_N, to obtain a plurality of edge enhanced images, denoted as L₀ . . . L<sub>N−

1</sub>, where

L_i=G_i−

G_i+1, for i=0 to N−

1; and

e) obtaining a lowest resolution image, denoted as L_N.

16. The method of claim 15, wherein the step of successively low-pass filtering includes successively convolving the highest resolution image, G₀, with a weighting function.

17. The method of claim 16, wherein the weighting function is one of a Gaussian weighting function, a triangular weighting function, a rectangular weighting function, a trimodal weighting function, or a broad Gaussian weighting function.

18. A method according to claim 15, further including the step of encoding the plurality of edge enhanced images and the lowest resolution image.

19. The method of claim 18, wherein the encoding step encodes according to one of a Huffman code, range code, arithmetic code, Unary code, Elias code, Fibonacci code, Golomb code, or Rice code.

20. The method of claim 15, wherein the first data set, ph₀, comprises the entire SAR phase history data, PH:

ph₀=PH.

21. The method of claim 15, wherein the first data set, ph₀, comprises a first sub-aperture set, denoted as SA₀, of the SAR phase history data, PH, and step (b) further includes multiplying the SAR phase history data, PH, by a function representing the first sub-aperture set, denoted as f_SA0, where

ph₀=PH·

f_SA0.

22. The method of claim 21, wherein the first sub-aperture set, SA₀, is chosen from one of a circularly symmetric Gaussian, a circularly asymmetric Gaussian, a rectangle, an annuli, or a sector.

23. A method of iterative target detection in a synthetic aperture radar (SAR) scene, the method comprising the steps of:

a) obtaining SAR phase history data, denoted as PH, corresponding to the scene;

b) integrating over a plurality of data sets, denoted as ph₀ . . . ph_N, of the phase history data, PH, to obtain a plurality of features, denoted as F₀ . . . F_N;

c) storing the plurality of features, F₀ . . . F_N;

d) performing target detection based on the stored plurality of features to obtain a receiver operating characteristic (ROC) of the scene;

e) determining if the ROC is acceptable; and

f) repeating steps (b)–

(e) for a new plurality of other data sets if the ROC is unacceptable.

24. The method of claim 23, wherein step (b3) includes inputting the plurality of features to one or more of a statistical, maximum likelihood classifier, and a neural network, packpropagation classifier.

25. The method of claim 24, wherein step (b3) further includes inputting one or more prototype vectors to the classifier(s), each vector corresponding to a respective target or class.

26. The method of claim 23, wherein the first data set, ph₀, and the plurality of other data sets, ph₁ . . . ph_N, include a respective plurality of sub-aperture sets, denoted as SA₀ . . . SA_N, of the SAR phase history data, PH, and the method further includes the step of:

choosing the plurality of sub-aperture sets, SA₀ . . . SA_N, from one or more of a circularly symmetric Gaussian, a circularly asymmetric Gaussian, a rectangle, an annuli, and a sector.

27. A method of progressively transmitting multi-resolution synthetic aperture radar (SAR) images, the method comprising the steps of:

a) obtaining SAR phase history data corresponding to a scene;

b) generating a correlated image pyramid including a plurality of multi-resolution images of the scene;

c) generating a Laplacian image pyramid including a plurality of Laplacian images corresponding to a difference of the multi-resolution images in successive levels of the correlated image pyramid;

d) encoding each one of the plurality of Laplacian images;

e) receiving, from a client, a request for a desired image of the scene;

f) transmitting each one of the plurality of Laplacian images, starting from a bottom level of the Laplacian image pyramid, until the desired image of the scene is transmitted.

28. A method according to claim 27, further including the steps of:

receiving an indication that the desired image of the scene has been transmitted, wherein step (f) stops transmitting once it receives the indication; and

repeating steps (b)–

(f) for a new correlated image pyramid if the indication is not received.

29. A method according to claim 27, further comprising the step of detecting one or more targets in the scene, wherein:

step (b) generates the correlated image pyramid comprising a multi-resolution images of the one or more targets;

step (e) receives a request for a desired image of a desired target of the one or more targets; and

step (f) transmits until the desired image of the desired target is transmitted.