IP Library Granted Patent US 12,189,022
Granted Patent B2
US 12,189,022 · App. 17/941,809 · Granted Jan 7, 2025

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.
G01S13/9021G06F18/214G06T5/70G06T7/70G06T15/205G06T17/05G06T2207/10044G06T2207/20081
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Quick Facts
Patent No.
US 12,189,022
App. No.
17/941,809
Granted
Jan 7, 2025
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 (46)

1. A method for identifying changes in synthetic aperture radar (SAR) imagery, the method comprising:

identifying one or more locations of detected change within a first set of SAR imagery of a scene having a first resolution;

accessing a second set of SAR imagery of the scene having a second resolution higher than the first resolution;

performing change detection within the second set of SAR imagery of the scene based on the identified one or more locations of detected change within the first set of SAR imagery of the scene; and

mapping one or more trends within locations of detected change within the second set of SAR imagery of the scene over time based on a time history of SAR imagery of the scene.

2. The method of claim 1 , wherein:

the method further comprises filtering the first set of SAR imagery of the scene to one or more filtered regions of the scene; and identifying the one or more locations of detected change within the first set of SAR imagery comprises identifying the one or more locations from among the one or more filtered regions of the scene.

3. The method of claim 2 , wherein filtering the first set of SAR imagery of the scene comprises filtering the first set of SAR imagery using a mask identifying different regions of the scene.

4. The method of claim 3 , wherein filtering the first set of SAR imagery using a mask comprises filtering the first set of SAR imagery using a land-use land-cover (LULC) mask.

5. The method of claim 3 , wherein filtering the first set of SAR imagery using a mask comprises filtering the first set of SAR imagery using a mask identifying a manner in which the different regions of the scene are used.

6. The method of claim 3 , further comprising receiving user input identifying the mask to be applied in the filtering.

7. The method of claim 3 , further comprising:

analyzing SAR imagery of the scene to identify regions of the scene; and

configuring the mask based on the regions identified in the analyzing.

8. The method of claim 7 , wherein analyzing the SAR imagery of the scene to identify the regions of the scene comprises analyzing the first set of SAR imagery of the scene and/or the second set of SAR imagery of the scene.

9. The method of claim 7 , wherein configuring the mask based on the one or more regions comprises adjusting the mask based on a change in shape of the one or more regions identified in the analyzing.

10. The method of claim 2 , wherein identifying the one or more locations of detected change comprises monitoring the one or more filtered regions of the scene over time to identify changes.

11. The method of claim 1 , further comprising:

mapping the locations of detected change within the second set of SAR imagery of the scene to a 3D model of the scene.

12. The method of claim 11 , further comprising:

quantifying magnitude of volumetric change within the scene based on the 3D model of the scene, wherein quantifying the magnitude of the volumetric change in the scene comprises performing the change detection over time; and

updating, based on the volumetric change, a geospatial location of one or more vertices on a second 3D model of the scene having a prior image geometry that is different from a current image geometry of the 3D model of the scene.

13. The method of claim 1 , wherein identifying one or more locations of detected change within the first set of SAR imagery comprises:

determining whether a change at a location is a transient change; and

in response to determining that the change at the location is a transient change, refraining from identifying the change at the location as a detected change.

14. The method of claim 13 , wherein determining whether a change at a location is a transient change comprises determining whether the change would have a lasting effect on topography of the scene.

15. The method of claim 1 , wherein:

the method further comprises analyzing the first set of SAR imagery to determine a rate of change within one or more portions of the scene; and

identifying one or more locations of detected change within a first set of SAR imagery comprises identifying a portion of the scene as a location of detected change in response to determining that the portion of the scene has a rate of change that meets at least one criterion.

16. The method of claim 1 , wherein identifying the one or more locations of detected change within the first set of SAR imagery and performing the change detection within the second set of SAR imagery comprises identifying 3D changes in the first set and second set of SAR imagery.

17. 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 identifying changes in synthetic aperture radar (SAR) imagery, the method comprising:

identifying one or more locations of detected change within a first set of SAR imagery of a scene having a first resolution;

accessing a second set of SAR imagery of the scene having a second resolution higher than the first resolution;

performing change detection within the second set of SAR imagery of the scene based on the identified one or more locations of detected change within the first set of SAR imagery of the scene; and

mapping one or more trends within locations of detected change within the second set of SAR imagery of the scene over time based on a time history of SAR imagery of the scene.

18. The at least one non-transitory computer-readable storage medium of claim 17 , wherein identifying the one or more locations of detected change within the first set of SAR imagery comprises identifying the one or more locations from among one or more regions of the scene that are less than all of the scene.

19. 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 identifying changes in synthetic aperture radar (SAR) imagery, the method comprising:

identifying one or more locations of detected change within a first set of SAR imagery of a scene having a first resolution;

accessing a second set of SAR imagery of the scene having a second resolution higher than the first resolution;

performing change detection within the second set of SAR imagery of the scene based on the identified one or more locations of detected change within the first set of SAR imagery of the scene; and

mapping one or more trends within locations of detected change within the second set of SAR imagery of the scene over time based on a time history of SAR imagery of the scene.

20. The apparatus of claim 19 , wherein identifying one or more locations of detected change within the first set of SAR imagery comprises:

determining whether a change at a location is a transient change; and

in response to determining that the change at the location is a transient change, refraining from identifying the change at the location as a detected change.

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 Sep 29, 2022
From: PENNINGS, JEFFREY SCOTT; KOSIANKA, JUSTYNA WERONIKA; MOODY, DANIELA IRINA
To: URSA SPACE SYSTEMS INC.
Reel/Frame 061250/0799 →
Continuity (4)
Division 17373429 · Jul 12, 2021
Division 16785409 · Feb 7, 2020
Provisional Application 62803320 · Feb 8, 2019
Related Publication 20230005218A1 · Jan 5, 2023
References Cited (65)
US 5745069A · Gail · 1998 [cited by applicant]
US 6388606B1 · Keydel et al. · 2002 [cited by applicant]
US 6571242B1 · Hao · 2003 [cited by examiner]
US 10203210B1 · Tagawa · 2019 [cited by examiner]
US 10345440B1 · West · 2019 [cited by examiner]
US 10495750B1 · Musgrove et al. · 2019 [cited by applicant]
US 10528542B2 · Banerjee · 2020 [cited by examiner]
US 10705205B2 · Wan et al. · 2020 [cited by applicant]
US 10754028B2 · Villano et al. · 2020 [cited by applicant]
US 11094114B2 · Pennings et al. · 2021 [cited by applicant]
US 11200689B1 · Smith et al. · 2021 [cited by applicant]
US 20030154060A1 · Damron · 2003 [cited by applicant]
US 20050052452A1 · Baumberg · 2005 [cited by applicant]
US 20070002138A1 · Oldroyd · 2007 [cited by applicant]
US 20080074312A1 · Cross et al. · 2008 [cited by applicant]
US 20100020066A1 · Dammann · 2010 [cited by applicant]
US 20110040754A1 · Peto · 2011 [cited by examiner]
US 20110228979A1 · Nishino · 2011 [cited by examiner]
US 20130082870A1 · Chambers et al. · 2013 [cited by applicant]
US 20130191082A1 · Barthelet et al. · 2013 [cited by applicant]
US 20130243296A1 · Nandi et al. · 2013 [cited by applicant]
US 20130300740A1 · Snyder et al. · 2013 [cited by applicant]
US 20140347213A1 · Nguyen et al. · 2014 [cited by applicant]
US 20140368688A1 · John Archibald · 2014 [cited by examiner]
US 20160061948A1 · Ton et al. · 2016 [cited by applicant]
US 20160103216A1 · Whelan et al. · 2016 [cited by applicant]
US 20160204840A1 · Liu · 2016 [cited by applicant]
US 20160259046A1 · Carlbom · 2016 [cited by examiner]
US 20160307073A1 · Moody · 2016 [cited by examiner]
US 20170010353A1 · Soofi et al. · 2017 [cited by applicant]
US 20170213342A1 · Klein · 2017 [cited by examiner]
US 20180053347A1 · Fathi et al. · 2018 [cited by applicant]
US 20180143311A1 · Melamed et al. · 2018 [cited by applicant]
US 20180158235A1 · Wu et al. · 2018 [cited by applicant]
US 20180245922A1 · Zaphir et al. · 2018 [cited by applicant]
US 20180336394A1 · Ouzounis · 2018 [cited by examiner]
US 20180348361A1 · Turbide · 2018 [cited by applicant]
US 20190034864A1 · Skaff · 2019 [cited by examiner]
US 20190072665A1 · Wang et al. · 2019 [cited by applicant]
US 20190101639A1 · Rincon et al. · 2019 [cited by applicant]
US 20190179009A1 · Klein · 2019 [cited by examiner]
US 20190329407A1 · Qi · 2019 [cited by examiner]
US 20190346556A1 · Wang et al. · 2019 [cited by applicant]
US 20200175270A1 · McKenna · 2020 [cited by examiner]
US 20200258296A1 · Pennings et al. · 2020 [cited by applicant]
US 20200294263A1 · Cho · 2020 [cited by examiner]
US 20210110157A1 · Sinha · 2021 [cited by examiner]
US 20210118097A1 · Guan · 2021 [cited by examiner]
US 20210149929A1 · Shen · 2021 [cited by examiner]
US 20210287037A1 · Chen et al. · 2021 [cited by applicant]
US 20210343076A1 · Pennings et al. · 2021 [cited by applicant]
US 20230005218A1 · Pennings · 2023 [cited by examiner]
US 20240037732A1 · Gong · 2024 [cited by examiner]
US 20240045025A1 · Laurila · 2024 [cited by examiner]
Gong et al., Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks, 2016 (Year: 2016). [cited by examiner]
Lim et al., Change Detection in High Resolution Satellite Images Using an Ensemble of Convolutional Neural Networks, 2018 (Year: 2018). [cited by examiner]
Jaturapitpornchai et al., Newly Built Construction Detection in SAR Images Using Deep Learning, 2019 (Year: 2019). [cited by examiner]
Balz et al., “Sar-Based 3D-Reconstruction of Complex Urban Environments,” 2003. [cited by applicant]
Kirscht et al., “3D Reconstruction of Buildings and Vegetation from Synthetic Aperture Radar (SAR) Images,” MVA, Nov. 1998, pp. 228-231. [cited by applicant]
Kusk et al., Synthetic SAR image generation using sensor, terrain and target models. In Proceedings of EUSAR 2016: 11th European Conference on Synthetic Aperture Radar Jun. 6, 2016:1-5. [cited by applicant]
Real et al., “A Novel Noise Removal Algorithm for Vertical Artifacts in Digital Elevation Models,” Environmental Science, 2013. [cited by applicant]
Reid et al., “Leveraging 3D models for SAR-based navigation in GPS-denied environments,” Algorithms for Synthetic Aperture Radar Imagery XXV, 2018, vol. 10647, pp. 128-138. [cited by applicant]
Remondino, Fabio., “Heritage recording and 3D modeling with photogrammetry and 3D scanning,” Remote Sensing, Jun. 2011, vol. 3, No. 6, pp. 1104-1138. [cited by applicant]
Yang et al., “Improving accuracy of automated 3-D building models for smart cities,” International Journal of Digital Earth, Feb. 1, 2019, vol. 12, No. 2, pp. 209-227. [cited by applicant]
Zheng et al., “Integrated Ground-Based SAR Interferometry, Terrestrial Laser Scanner, and Corner Reflector Deformation Experiments,” Sensors, Dec. 2018, vol. 18, No. 12, p. 4401. [cited by applicant]