High Volume Earth Observation Image Processing

• US 20090232349A1
• Filed: 01/07/2009
• Published: 09/17/2009
• Est. Priority Date: 01/08/2008
• Status: Active Grant

• Current Assignee: PCI Geomatics Enterprises, Inc. (PCI Geomatics Group Incorporated)
• Original Assignee: PCI Geomatics Enterprises, Inc. (PCI Geomatics Group Incorporated)
• Inventors: Pollock, Richard, Moses, Robert, Stanley, David, Burry, Louis
• Granted Patent: US 8,768,104 B2
• Time in Patent Office: Days
• US Class Current: 382/100
• CPC Class Codes: G06F 9/5072 Grid computing
• CPC Class Codes: G06T 2207/10032 Satellite or aerial image; Remote sensing
• CPC Class Codes: G06T 2207/30181 Earth observation
• CPC Class Codes: G06T 5/006 Geometric correction correction of chromatic aberrations adapted to be used in an image pickup device containing an electronic image sensor H04N23/10; detecting, correcting, reducing or removing artefacts resulting only from the lens unit, e.g. flare, shading, vignetting or "cos4" H04N25/61
• CPC Class Codes: G06T 5/80 Geometric correction
• CPC Class Codes: G06T 7/33 using feature-based methods
• CPC Class Codes: G06T 7/35 using statistical methods

Abstract

The present invention is related to the processing of data, and more particularly to a method of and system for processing large volumes of Earth observation imagery data. A system for processing a large volume of Earth observation imaging data is described, comprising a computer including a visual display and a user interface, a plurality of servers, an image database storing said Earth observation imaging data as a plurality of separate image data files, and a network for interconnecting the computer, plurality of servers and image database. The plurality of servers is operable to process the separate data files in a distributed manner, at least one of the plurality of servers is operable to process the separate data files in a multiprocessing environment and at least one of the plurality of servers is operable to collate the processed separate data files into a single imaging result.

Claims

1. A system for processing a large volume of Earth observation imaging data comprising:

a computer including a visual display and a user interface;

a plurality of compute servers;

an image database storing said Earth observation imaging data as a plurality of separate image data files;

a network for interconnecting said computer, said plurality of compute servers and said image database;

said plurality of compute servers being operable to process said separate data files in a distributed manner;

at least one of said plurality of compute servers being operable to process said separate data files in a multiprocessing environment; and

at least one of said plurality of compute servers being operable to collate processed separate data files into a single imaging result.

2. The system of claim 1, further comprising a central job database for storing a list of jobs to be performed, said jobs defining said processing to be performed, and said plurality of compute servers being operable to request jobs from said list.

3. The system of claim 2, wherein said central job database is further operable to store job status, history data and metadata, said plurality of compute servers being operable to send job status, history data and metadata to said central job database.

4. The system of claim 3, wherein said central job database comprises a relational database.

5. The system of claim 2, wherein said plurality of compute servers are operable to request jobs from said list when they have capacity.

6. The system of claim 5, wherein said plurality of compute servers are operable to request jobs from said list during particular specified times.

7. The system of claim 5, wherein each of said plurality of compute servers includes a configuration file specifying the number of jobs which may be run.

8. The system of claim 2, wherein said jobs comprise scripts.

9. The system of claim 8, wherein said scripts call stored processing modules having a standard application programming interface (API).

10. The system of claim 9, further comprising an interface generator operable to translate the API and documentation to the particulars and syntax of a desired scripting language, allowing processing modules to be called from scripts written in different languages.

11. The system of claim 9, wherein said processing modules are operable to receive input data from local files, databases, memory objects or Web services.

12. The system of claim 1, wherein said network includes a local area network (LAN) and at least one of said plurality of compute servers is accessible via said LAN.

13. The system of claim 1, wherein said network includes an Internet network and at least one of said plurality of compute servers is accessible via the Internet.

14. The system of claim 1, wherein at least one of said plurality of compute servers is operable to process said separate data files using specialized internal hardware.

15. The system of claim 14, wherein said specialized internal hardware is selected from the group consisting of multiple CPUs, one or more Graphical Processing Units (GPUs) or a Cell processor.

16. The system of claim 14, wherein at least one of said plurality of compute servers is operable to process said separate data files using symmetric multi-processing (SMP).

17. The system of claim 1, wherein said image database comprises a parallel virtual file system (PVFS).

37. The system of claim 1, wherein image classification is performed, each of said plurality of compute servers processing said separate data files in a distributed manner, performing a different classification method.

38. The system of claim 37, wherein said at least one of said plurality of compute servers operable to collate processed separate data files, is operable to execute a voting scheme to select the output of one of said different classification methods as an optimal result.

18. A method for processing a large volume of Earth observation imaging data comprising the steps of:

storing said Earth observation imaging data as a plurality of separate image data files on an image database;

processing said separate data files in a distributed manner on a plurality of compute servers, said plurality of compute servers and said image database interconnected by a network;

at least one of said plurality of compute servers processing said separate data files using multiprocessing;

collating said processed separate data files into a single imaging result on one of said plurality of compute servers; and

interfacing with said single imaging result via a computer including a visual display and a user interface.

19. The method of claim 18, further comprising the steps of:

storing a list of jobs to be performed, on a central job database, said jobs defining said processing to be performed; and

said plurality of compute servers requesting jobs from said list.

20. The method of claim 19, further comprising the steps of:

said plurality of compute servers sending job status, history data and metadata to said central job database; and

said central job database storing received job status, history data and metadata.

21. The method of claim 20, wherein said central job database comprises a relational database.

22. The method of claim 20, wherein said step of said plurality of compute servers requesting jobs from said list comprises the step of said plurality of compute servers requesting jobs from said list when they have capacity.

23. The method of claim 22, wherein said step of said plurality of compute servers requesting jobs from said list comprises the step of said plurality of compute servers requesting jobs from said list during particular specified times.

24. The method of claim 22, further comprising the step of said plurality of compute servers referring to a configuration file specifying the number of jobs which may be run, in determining whether they have capacity.

25. The method of claim 19, wherein said jobs comprise scripts.

26. The method of claim 25, further comprising the step of said scripts calling stored processing modules having a standard application programming interface (API).

27. The method of claim 26, further comprising the step of an interface generator operable translating the API and documentation to the particulars and syntax of a desired scripting language, allowing processing modules to be called from scripts written in different languages.

28. The method of claim 26, further comprising the step of said processing modules obtaining input data from local files, databases, memory objects or Web services.

29. The method of claim 18, wherein said network includes a local area network (LAN) and at least one of said plurality of compute servers is accessible via said LAN.

30. The method of claim 18, wherein said network includes an Internet network and at least one of said plurality of compute servers is accessible via the Internet.

31. The method of claim 18, further comprising the step of at least one of said plurality of compute servers processing said separate data files using specialized internal hardware.

32. The method of claim 30, wherein said specialized internal hardware comprises at least one selected from the group consisting of multiple CPUs, Graphical Processing Units (GPUs) or Cell processors.

33. The method of claim 30, wherein said step of processing said separate data files using multiprocessing, comprises the step of processing using symmetric multiple processing (SMP).

34. The method of claim 18, wherein said image database comprises a parallel virtual file system (PVFS).

35. The method of claim 18, wherein said processing comprises image classification, and said step of processing said separate data files in a distributed manner comprises the step of performing a different classification method on each of said plurality of compute servers.

36. The method of claim 35, wherein said step of collating comprises the step of executing a voting scheme to select the output of one of said different classification methods as an optimal result.