Multisensor registration of Earth remotely sensed imagery

Jacqueline Le Moigne, Arlene Cole-Rhodes, Roger Eastman, Kisha Johnson, Jeffrey T. Morisette, Nathan S. Netanyahu, Harold S. Stone, Ilya Zavorin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Assuming 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+ (30 m), MODIS (500 m), and SeaWIFS (1000m).
Original languageAmerican English
Title of host publicationInternational Symposium on Remote Sensing
PublisherInternational Society for Optics and Photonics
StatePublished - 2002

Bibliographical note

Place of conference:Toulouse, France


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