Automated metric information network

• US 9,251,419 B2
• Filed: 02/07/2013
• Issued: 02/02/2016
• Est. Priority Date: 02/07/2013
• Status: Active Grant

• Current Assignee: Maxar Intelligence Incorporated (Galileo Parent, Inc.)
• Original Assignee: DigitalGlobe, Inc. (Galileo Parent, Inc.)
• Inventors: Johnston, Carolyn P., Sjahputera, Ozy, Bleiler, Laurence C., Bader, Brett W., Smith, James Thomas II
• Primary Examiner(s): Bitar, Nancy
• Application Number:
  US13/762,355
• Publication Number:
  US 20140219514A1
• Time in Patent Office: 1,090 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G01C 11/06 by comparison of two or more pictures of the same area
• CPC Class Codes: G06V 20/13 Satellite images
• CPC Class Codes: G06V 30/422 Technical drawings; Geographical maps

Abstract

A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.

Claims

1. A method of selecting Ground Control Points (GCPs) for a geodetic network, comprising:

receiving more than one image of a given area on the ground;

detecting interest points in each image and generating an interest point descriptor and a geo-location for each interest point;

clustering the interest points based on geo-location to create clusters of interest points;

comparing the interest point descriptors for the interest points within each cluster;

grouping at least some of the interest points based on the comparison;

and selecting certain groups as GCPs;

wherein the selected GCPs are from a set of images where every image in the set overlaps at least a portion of another image in the set, each one of the plurality of GCPs having a geo-coordinate where the GCP is located in the image and each one of the plurality of GCPs having an error covariance that exist between that one of the plurality of GCPs and every other GCP, wherein the plurality of GCPs form a geodetic network; and

further including receiving additional information about the geo-coordinate of one or more of the GCPs; and

after receiving the additional information, updating the geo-coordinate and error covariance of each GCP in the network, wherein the updating is performed without inline image processing.

2. A method as defined in claim 1, further including generating one or more additional GCPs, each one of the one or more additional GCPs having a geo-coordinate where the GCP is located in the image and each one of the one or more additional GCPs having a error covariance that is exist between that one of the one or more additional GCPs and every other GCP.

3. A method as defined in claim 1, wherein one or more of the GCPs are identified from an automated tie pointing process.

4. A method as defined in claim 1, wherein the updating operation includes bundle adjustment.

5. A method as defined in claim 4, wherein the bundle adjustment adjusts each of the geo-coordinates and generates an error covariance between each of the geo-coordinates.

6. A method as defined in claim 1, wherein the ground control points are generated from satellite or aerial images of the ground by an imaging system that can determine the geo-coordinate of locations in the image.

7. A method as defined in claim 1, wherein the geo-coordinates are expressed in a three-dimensional coordinate system.

8. A method as defined in claim 1, wherein the generating operation includes clustering interest points based on geo-location.

9. A method as defined in claim 8, wherein the generating operation includes comparing interest point descriptors for interest points within each cluster.

10. A method as defined in claim 8, wherein the generating operation is performed without human intervention.

11. A method of selecting Ground Control Points (GCPs) for a geodetic network, comprising:

with a computer, receiving more than one photographic image of a given area on the ground;

with a computer, detecting interest points in each photographic image and generating an interest point descriptor and a geo-location for each interest point;

with a computer, clustering the interest points in the more than one photographic image based on geo-location to create clusters of interest points, wherein at least one of the clusters includes at least one interest point from a first one of the more than one photographic image and at least one interest point from a second one of the more than one photographic image;

with a computer, comparing the interest point descriptors for the interest points within each cluster;

with a computer, grouping at least some of the interest points based on the comparison;

with a computer, selecting certain groups as GCPs;

further storing image data and geo-location information about each ground control point in a tie point database; and

performing a sequential fusion of block adjustments from the tie point database, without performing inline measurement of ground control points into new imagery.

14. A method as defined in claim 11, wherein the generating ground control points operation includes collecting image observations from the set S of imagery by first grouping image observations together according to the geographic area in which the observations are found, and then processing the grouped image observations in parallel on one or more computers to find groups of tie points.

12. A method of selecting Ground Control Points (GCPs) for a geodetic network, comprising:

receiving more than one image of a given area on the ground;

detecting interest points in each image and generating an interest point descriptor and a geo-location for each interest point;

clustering the interest points based on geo-location to create clusters of interest points;

comparing the interest point descriptors for the interest points within each cluster;

grouping at least some of the interest points based on the comparison; and

selecting certain groups as GCPs;

further storing image data and geo-location information about each ground control point in a tie point database; and

performing a sequential fusion of block adjustments from the tie point database, without performing inline measurement of ground control points into new imagery;

wherein the first time that the block adjustment is performed, the method includes;

specifying the images from the set S that are to be used in the first block adjustment;

using solely the information and data in the tie point database to perform the block adjustment, without the use of any inline image processing, in order to adjust the geo-locations of the ground control points and generate posterior covariances between each of the ground control points; and

storing the adjusted ground control point geo-locations, and their posterior covariances, in a ground control network database.

13. A method as defined in claim 12, wherein subsequent times that the block adjustment is performed, the method includes:

specifying the images to be used in the current block adjustment;

using solely the information and data in the tie point database to perform the block adjustment, without the use of any inline image processing, in order to adjust the geo-locations of the ground control points and generate posterior covariances between each of the ground control points; and

storing the adjusted ground control point geo-locations, and their posterior covariances, in the ground control network database.