TY - GEN
T1 - Multisensor registration of Earth remotely sensed imagery
AU - Le Moigne, Jacqueline
AU - Cole-Rhodes, Arlene
AU - Eastman, Roger
AU - Johnson, Kisha
AU - Morisette, Jeffrey T.
AU - Netanyahu, Nathan S.
AU - Stone, Harold S.
AU - Zavorin, Ilya
N1 - Place of conference:Toulouse, France
PY - 2002
Y1 - 2002
N2 - 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).
AB - 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).
UR - https://scholar.google.co.il/scholar?q=Multi-Sensor+Registration+of+Earth+Remotely+Sensed+Imagery&btnG=&hl=en&as_sdt=0%2C5
M3 - Conference contribution
BT - International Symposium on Remote Sensing
PB - International Society for Optics and Photonics
ER -