System and method for fusing vector data with imagery

• US 8,340,360 B2
• Filed: 06/10/2011
• Issued: 12/25/2012
• Est. Priority Date: 07/09/2004
• Status: Expired due to Fees

• Current Assignee: University of Southern California
• Original Assignee: University of Southern California
• Inventors: Chen, Ching-Chien, Kapoor, Dipsy, Knoblock, Craig A., Shahabi, Cyrus
• Primary Examiner(s): Bella, Matthew
• Assistant Examiner(s): Li, Ruiping
• Application Number:
  US13/158,301
• Publication Number:
  US 20110280453A1
• Time in Patent Office: 564 Days
• Field of Search: 382/103, 382/113, 382/190, 382/294, 382/201, 707/727, 707/741, 707/736
• US Class Current: 382/113
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30184 Infrastructure
• CPC Class Codes: G06T 7/75 involving models

Abstract

Automatic conflation systems and techniques which provide vector-imagery conflation and map-imagery conflation. Vector-imagery conflation is an efficient approach that exploits knowledge from multiple data sources to identify a set of accurate control points. Vector-imagery conflation provides automatic and accurate alignment of various vector datasets and imagery, and is appropriate for GIS applications, for example, requiring alignment of vector data and imagery over large geographical regions. Map-imagery conflation utilizes common vector datasets as “glue” to automatically integrate street maps with imagery. This approach provides automatic, accurate, and intelligent images that combine the visual appeal and accuracy of imagery with the detailed attribution information often contained in such diverse maps. Both conflation approaches are applicable for GIS applications requiring, for example, alignment of vector data, raster maps, and imagery. If desired, the conflated data generated by such systems may be retrieved on-demand.

Claims

1. A method for automatically conflating vector data with geospatial data, said method comprising:

identifying a first set of feature points associated with said vector data;

identifying a second set of feature points associated with said geospatial data;

generating control point pairs, wherein each of said control point pairs includes a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points; and

deforming at least one of said vector data and said geospatial data to effectively align said control point pairs, wherein said second set of feature points are identified according to a method comprising;

generating a template based upon at least one of said first set of feature points and line characteristics associated with said at least one of said first set of feature points;

identifying geo-coordinates of said first set of feature points;

identifying approximate locations within said geospatial data of each of said first set of feature points;

generating a road-labeled image by identifying on-road regions of said geospatial data; and

comparing said road-labeled image with said template to identify on-road regions in said road-labeled image which have effectively similar dimensions and angles, relative to an orientation of said vector data, as said template, said comparing resulting in said identifying of said second set of feature points.

2. The method according to claim 1, wherein said first set of feature points are identified according to a method comprising:

identifying a plurality of line segments defined by said vector data;

identifying a plurality candidate control points, which are each defined by an endpoint of one of said plurality of line segments; and

determining line connectivity of each of said plurality of candidate control points, wherein if said line connectivity for a particular candidate control point exceeds a threshold, then said particular candidate control point is identified as one of said first set of feature points.

3. The method according to claim 2, wherein said threshold is exceeded whenever more than two line segments of said plurality of line segments share common candidate control points of said plurality of candidate control points.

4. The method according to claim 1, wherein said line characteristics comprise line width of a line segment associated with said at least one of said first set of feature points.

5. The method according to claim 1, wherein said line characteristics comprise line angle of a line segment associated with said at least one of said first set of feature points, wherein said line angle is defined by the angle of said line segment relative to an orientation of said vector data.

6. The method according to claim 1, wherein said generating control point pairs comprises:

filtering inaccurate control point pairs from said control point pairs, each of said inaccurate control point pairs including a feature point of said second set of feature points that is located on said geospatial data that exceeds a threshold distance and angle relative to an actual location on said geospatial data.

7. The method according to claim 1, said method further comprising:

generating a plurality of control point vectors, each of said plurality of control point vectors being defined by a relative position of a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points;

generating a vector median for said plurality of control point vectors;

associating each of said control point pairs with corresponding control point vectors of said plurality of control point vectors; and

filtering inaccurate control point pairs from said control point pairs, each of said inaccurate control point pairs having a corresponding control point vector which exceeds a threshold difference in direction and magnitude relative to said vector median.

8. The method according to claim 1, said method further comprising:

(a) generating a plurality of control point vectors which are individually associated with a particular one of said control point pairs, wherein each of said plurality of control point vectors comprises a tail point defined by a distinct one of said first set of feature points and a head point defined by a corresponding distinction one of said second set of feature points;

(b) calculating a number of said tail points of said plurality of control point vectors that are located within a predetermined distance relative to a particular head point associated with a particular one of said plurality of control point vectors, wherein if said number falls below a certain threshold, then a control point pair that is associated with said particular one of said plurality of control point vectors is identified as an inaccurate control point pair;

(c) filtering said inaccurate control point pair from said control point pairs; and

(d) repeating said calculating operation (b) and said filtering operation (c) for each tail point of said plurality of control point vectors.

9. The method according to claim 1, wherein said deforming includes rubber sheeting at least one of said vector data and said geospatial data to effectively align said control point pairs.

10. The method according to claim 1, said method further comprising:

obtaining said vector data from a vector data source; and

obtaining said geospatial data from a geospatial data source.

11. The method according to claim 1, wherein said deforming deforms said vector data to effectively align said control point pairs.

12. The method according to claim 1, wherein said deforming deforms said geospatial data to effectively align said control point pairs.

13. The method according to claim 1, wherein said deforming deforms both said vector data and said geospatial data to effectively align said control point pairs.

14. The method according to claim 1, wherein said vector data comprises road network vector data.

15. The method according to claim 1, wherein said geospatial data comprises orthoimagery.

16. The method according to claim 1, said method further comprising:

creating image-aligned vector data based upon said deforming.

17. The method according to claim 16, said method further comprising:

generating a composite image comprising said geospatial data and said image aligned vector data.

18. The method according to claim 16, said method further comprising:

generating a composite image labeled with attribute data associated with said image-aligned vector data.

19. The method according to claim 1, said method further comprising:

finding geo-coordinates and scale of said vector data.

20. The method according to claim 1, wherein said geospatial data comprises vector data.

21. The method according to claim 1, wherein said geospatial data comprises georeferenced imagery.

22. The method according to claim 1, wherein said geospatial data comprises a raster map.

23. The method according to claim 1, wherein each of said first set of feature points comprises a distinct road intersection, and wherein each of said second set of feature points comprises a distinct road intersection.

24. The method according to claim 1, wherein each of said first set of feature points comprises a distinct railroad intersection, and wherein each of said second set of feature points comprises a distinct railroad intersection.

25. The method according to claim 1, wherein each of said first set of feature points comprises a distinct river intersection, and wherein each of said second set of feature points comprises a distinct river intersection.

26. A non-transitory computer-readable storage medium containing instructions for controlling a computer for conflating vector data with geospatial data according to a method comprising:

identifying a first set of feature points associated with said vector data;

identifying a second set of feature points associated with said geospatial data;

generating control point pairs, wherein each of said control point pairs includes a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points;

deforming at least one of said vector data and said geospatial data to effectively align said control point pairs;

generating a plurality of control point vectors, each of said plurality of control point vectors being defined by a relative position of a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points;

generating a vector median for said plurality of control point vectors;

associating each of said control point pairs with corresponding control point vectors of said plurality of control point vectors; and

filtering inaccurate control point pairs from said control point pairs, each of said inaccurate control point pairs having a corresponding control point vector which exceeds a threshold difference in direction and magnitude relative to said vector median.

27. A system for conflating vector data with geospatial data, said system comprising:

means for identifying a first set of feature points associated with said vector data;

means for identifying a second set of feature points associated with said geospatial data;

means for generating control point pairs, wherein each of said control point pairs includes a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points;

means for deforming at least one of said vector data and said geospatial data to effectively align said control point pairs;

means for generating a plurality of control point vectors which are individually associated with a particular one of said control point pairs, wherein each of said plurality of control point vectors comprises a tail point defined by a distinct one of said first set of feature points and a head point defined by a corresponding distinction one of said second set of feature points;

means for calculating a number of said tail points of said plurality of control point vectors that are located within a predetermined distance relative to a particular head point associated with a particular one of said plurality of control point vectors, wherein if said number falls below a certain threshold, then a control point pair that is associated with said particular one of said plurality of control point vectors is identified as an inaccurate control point pair;

means for filtering said inaccurate control point pair from said control point pairs; and

means for repeating said calculating operation (b) and said filtering operation (c) for each tail point of said plurality of control point vectors.

28. A method for automatically conflating vector data with geospatial data, said method comprising:

identifying a first set of feature points associated with said vector data;

identifying a second set of feature points associated with said geospatial data;

generating control point pairs, wherein each of said control point pairs includes a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points; and

deforming at least one of said vector data and said geospatial data to effectively align said control point pairs, said second set of feature points being identified according to a method comprising;

generating a template based upon at least one of said first set of feature points and line characteristics associated with said at least one of said first set of feature points;

identifying geo-coordinates of said first set of feature points;

identifying approximate locations within said geospatial data of each of said first set of feature points;

generating a road-labeled image by identifying on-road regions of said geospatial data; and

comparing said road-labeled image with said template to identify on-road regions in said road-labeled image which have effectively similar dimensions and angles, relative to an orientation of said vector data, as said template, said comparing resulting in said identifying of said second set of feature points, said identifying of said on-road regions of said geospatial data being accomplished by a method comprising;

computing a hue value for a plurality of locations on said geospatial data; and

for each of said plurality of locations, comparing said hue value with previously calculated hue values obtained from representative geospatial data, comprising on-road and off-road regions, to generate a probability that a particular location of said plurality of locations is either on-road or off-road, said on-road regions of said geospatial data being defined by said plurality of locations on said geospatial data which meet a certain threshold of said probability.

29. A method for automatically conflating vector data with geospatial data, said method comprising:

identifying a first set of feature points associated with said vector data;

identifying a second set of feature points associated with said geospatial data;

generating control point pairs, wherein each of said control point pairs includes a distinct one of said first set of feature points and a corresponding distinct one of said second set of feature points; and

deforming at least one of said vector data and said geospatial data to effectively align said control point pairs, said second set of feature points being identified according to a method comprising;

generating a template based upon at least one of said first set of feature points and line characteristics associated with said at least one of said first set of feature points;

identifying geo-coordinates of said first set of feature points;

identifying approximate locations within said geospatial data of each of said first set of feature points;

generating a road-labeled image by identifying on-road regions of said geospatial data; and

comparing said road-labeled image with said template to identify on-road regions in said road-labeled image which have effectively similar dimensions and angles, relative to an orientation of said vector data, as said template, said comparing resulting in said identifying of said second set of feature points said identifying of said on-road regions of said geospatial data being accomplished by a method comprising;

computing a saturation density for a plurality of locations on said geospatial data; and

for each of said plurality of locations, comparing said saturation density with previously calculated saturation densities obtained from representative geospatial data, comprising on-road and off-road regions, to generate a probability that a particular location of said plurality of locations is either on-road or off-road, said on-road regions of said geospatial data being defined by said plurality of locations on said geospatial data which meet a certain threshold of said probability.

30. The method according to claim 29, wherein said method for identifying said on-road regions of said geospatial data further comprises:

computing a hue value for a plurality of locations on said geospatial data;

for each of said plurality of locations, comparing said hue value with previously calculated hue values obtained from representative geospatial data, comprising on-road and off-road regions, to generate a probability that a particular location of said plurality of locations is either on-road or off-road.