IP Library Granted Patent US 11,461,964
Granted Patent B2
US 11,461,964 · App. 17/373,429 · Granted Oct 4, 2022

Satellite SAR artifact suppression for enhanced three-dimensional feature extraction, change detection, and visualizations

Inventors: Jeffrey Scott Pennings (Ann Arbor, MI); Justyna Weronika Kosianka (Staten Island, NY); Daniela Irina Moody (Los Alamos, NM)
Assignee: Ursa Space Systems Inc.
G06T17/05G01S13/9021G06K9/6256G06T5/002G06T7/70G06T15/205G06T2207/10044G06T2207/20081
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Quick Facts
Patent No.
US 11,461,964
App. No.
17/373,429
Granted
Oct 4, 2022
Kind
B2
Abstract

Systems and methods for satellite Synthetic Aperture Radar (SAR) artifact suppression for enhanced three-dimensional feature extraction, change detection, and/or visualizations are described. In some aspects, the described systems and methods include a method for suppressing artifacts from complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for creating a photo-realistic 3D model of a scene based on complex SAR data associated with a scene. In some aspects, the described systems and methods include a method for identifying three-dimensional (3D) features and changes in SAR imagery.

Claims (44)

1. A method for creating a photo-realistic 3D model of a scene based on complex Synthetic Aperture Radar (SAR) data associated with the scene, the method comprising:

mapping the complex SAR data associated with the scene to a 3D model of the scene;

suppressing one or more SAR artifacts from the complex SAR data based on the mapping to obtain cleaned complex SAR data;

resampling the cleaned complex SAR data to generate one or more two-dimensional (2D) SAR images of the scene; and

generating the photo-realistic 3D model of the scene based on the one or more 2D SAR images of the scene.

2. The method of claim 1 , wherein resampling the cleaned complex SAR data comprises resampling In-phase (I) and Quadrature-phase (Q) data of the complex SAR data to generate the one or more 2D images.

3. The method of claim 1 , wherein generating the photo-realistic 3D model of the scene comprises creating a photo-realistic 3D model of the scene at least in part by using perspective modeling with the one or more 2D SAR images to simulate the scene from one or more viewing angles.

4. The method of claim 1 , wherein generating the photo-realistic 3D model of the scene comprises generating the photo-realistic 3D model using InSAR and/or stereo radargrammetry.

5. The method of claim 1 , wherein generating the photo-realistic 3D model of the scene comprises updating a photogrammetric 3D model of the scene.

6. The method of claim 1 , further comprising:

generating a library of training data associated with the scene, wherein generating the library comprises:

obtaining simulated SAR data labeled with objects and features within the scene;

associating 2D imagery data with a high-fidelity 3D surface model of the scene and 3D objects within the scene through one or more machine learning search algorithms; and

extracting 3D features and objects from the simulated SAR data based on the photo-realistic 3D model of the scene.

7. An apparatus comprising:

at least one processor; and

at least one storage medium having encoded thereon executable instructions that, when executed by the at least one processor, cause the at least one processor to carry out a method for creating a photo-realistic 3D model of a scene based on complex Synthetic Aperture Radar (SAR) data associated with the scene, the method comprising:

mapping the complex SAR data associated with the scene to a 3D model of the scene;

suppressing one or more SAR artifacts from the complex SAR data based on the mapping to obtain cleaned complex SAR data;

resampling the cleaned complex SAR data to generate one or more two-dimensional (2D) SAR images of the scene; and

generating the photo-realistic 3D model of the scene based on the one or more 2D SAR images of the scene.

8. The apparatus of claim 7 , wherein resampling the cleaned complex SAR data comprises resampling In-phase (I) and Quadrature-phase (Q) data of the complex SAR data to generate the one or more 2D images.

9. The apparatus of claim 7 , wherein generating the photo-realistic 3D model of the scene comprises creating a photo-realistic 3D model of the scene at least in part by using perspective modeling with the one or more 2D SAR images to simulate the scene from one or more viewing angles.

10. The apparatus of claim 7 , wherein generating the photo-realistic 3D model of the scene comprises generating the photo-realistic 3D model using InSAR and/or stereo radargrammetry.

11. The apparatus of claim 7 , wherein generating the photo-realistic 3D model of the scene comprises updating a photogrammetric 3D model of the scene.

12. The apparatus of claim 7 , wherein the method further comprises:

generating a library of training data associated with the scene, wherein generating the library comprises:

obtaining simulated SAR data labeled with objects and features within the scene;

associating 2D imagery data with a high-fidelity 3D surface model of the scene and 3D objects within the scene through one or more machine learning search algorithms; and

extracting 3D features and objects from the simulated SAR data based on the photo-realistic 3D model of the scene.

13. At least one non-transitory computer-readable storage medium having encoded thereon executable instructions that, when executed by at least one processor, cause the at least one processor to carry out a method for creating a photo-realistic 3D model of a scene based on complex Synthetic Aperture Radar (SAR) data associated with the scene, the method comprising:

mapping the complex SAR data associated with the scene to a 3D model of the scene;

suppressing one or more SAR artifacts from the complex SAR data based on the mapping to obtain cleaned complex SAR data;

resampling the cleaned complex SAR data to generate one or more two-dimensional (2D) SAR images of the scene; and

generating the photo-realistic 3D model of the scene based on the one or more 2D SAR images of the scene.

14. The non-transitory computer-readable storage medium of claim 13 , wherein resampling the cleaned complex SAR data comprises resampling In-phase (I) and Quadrature-phase (Q) data of the complex SAR data to generate the one or more 2D images.

15. The non-transitory computer-readable storage medium of claim 13 , wherein generating the photo-realistic 3D model of the scene comprises creating a photo-realistic 3D model of the scene at least in part by using perspective modeling with the one or more 2D SAR images to simulate the scene from one or more viewing angles.

16. The non-transitory computer-readable storage medium of claim 13 , wherein generating the photo-realistic 3D model of the scene comprises generating the photo-realistic 3D model using InSAR and/or stereo radargrammetry.

17. The non-transitory computer-readable storage medium of claim 13 , wherein generating the photo-realistic 3D model of the scene comprises updating a photogrammetric 3D model of the scene.

18. The non-transitory computer-readable storage medium of claim 13 , wherein the method further comprises:

generating a library of training data associated with the scene, wherein generating the library comprises:

obtaining simulated SAR data labeled with objects and features within the scene;

associating 2D imagery data with a high-fidelity 3D surface model of the scene and 3D objects within the scene through one or more machine learning search algorithms; and

extracting 3D features and objects from the simulated SAR data based on the photo-realistic 3D model of the scene.

Assignments (2)
SECURITY INTEREST Recorded Nov 4, 2024
From: URSA SPACE SYSTEMS INC.
To: HORIZON TECHNOLOGY FINANCE CORPORATION
Reel/Frame 069296/0486 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 12, 2021
From: PENNINGS, JEFFREY SCOTT; KOSIANKA, JUSTYNA WERONIKA; MOODY, DANIELA IRINA
To: URSA SPACE SYSTEMS INC.
Reel/Frame 056828/0435 →
Continuity (3)
Division 16785409 · Feb 7, 2020
Provisional Application 62803320 · Feb 8, 2019
Related Publication 20210343076A1 · Nov 4, 2021
Cited By (2)
US 12,306,293 US 12,554,901