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Multi-Sensor Registration of Earth Remotely Sensed ImageryAssuming that approximate registration is given within a few pixels by a systematic correction system, we develop automatic image registration methods for multi-sensor data with the goal of achieving sub-pixel accuracy. Automatic image registration is usually defined by three steps; feature extraction, feature matching, and data resampling or fusion. Our previous work focused on image correlation methods based on the use of different features. In this paper, we study different feature matching techniques and present five algorithms where the features are either original gray levels or wavelet-like features, and the feature matching is based on gradient descent optimization, statistical robust matching, and mutual information. These algorithms are tested and compared on several multi-sensor datasets covering one of the EOS Core Sites, the Konza Prairie in Kansas, from four different sensors: IKONOS (4m), Landsat-7/ETM+ (30m), MODIS (500m), and SeaWIFS (1000m).
Document ID
Document Type
LeMoigne, Jacqueline (NASA Goddard Space Flight Center Greenbelt, MD United States)
Cole-Rhodes, Arlene (Morgan State Univ. Baltimore, MD United States)
Eastman, Roger (Loyola Coll. Baltimore, MD United States)
Johnson, Kisha (Morgan State Univ. Baltimore, MD United States)
Morisette, Jeffrey (Science Systems and Applications, Inc. Greenbelt, MD United States)
Netanyahu, Nathan S. (Bar-Ilan Univ. Ramat-Gan, Israel)
Stone, Harold S. (NASA Goddard Space Flight Center Greenbelt, MD United States)
Zavorin, Ilya (NASA Goddard Space Flight Center Greenbelt, MD United States)
Zukor, Dorothy
Date Acquired
September 7, 2013
Publication Date
January 1, 2001
Subject Category
Instrumentation and Photography
Funding Number(s)
Distribution Limits
Work of the US Gov. Public Use Permitted.

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