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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Southern University Office of Grants and Sponsored Programs Southern Branch Post Office Baton Rouge, LA 70813 SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) 10. SPONSOR/MONITOR'S ACRONYM(S)AFRL/HEX SPONSOR/MONITOR'S REPORT NUMBER(S)Air SUPPLEMENTARY NOTES03-15-07 Cleared for public release: PA-07-091 ABSTRACTThe objective of this project was to investigate methods to recover the maximum amount of available information from an image. Some radio frequency and optical sensors collect large-scale sets of spatial imagery data whose content is often obscured by fog, clouds, foliage and other intervening structures. Often, the obstruction is such as to render unreliable the definition of underlying images. Various mathematical operations used in image processing to remove obstructions from images and to recover reliable information were investigated, to include Spatial Domain Processing, Frequency Domain Processing, and non-Abelian group operations. These imaging techniques were researched and their effectiveness determined. Some of the most effective techniques were selected, refined, extended and customized for this project. THIS PAGE INTENTIONALLY LEFT BLANKiv

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Large-scale image data needs to be organized to allow efficient browsing, searching and retrieval. It is also critical for military and intelligence applications especially suited to addressing national security, that is, image data are critical for situation awareness and assessment purposes, and they are invaluable for detecting changes and providing retevant information to decision makers. Currently, there is a lack of comprehensive tools that can allow fast and efficient processing of information from huge image data. In order to fill this gap, this paper proposes a novel method based combined coIor features for large-scale spatial image retrieval by considering divided smaller sub-images. Several retrieval testing results are also given to show the efficiency for the new method. (This research was supported by ORNL /ORAU 2003 summer research program)