One-sided ascending or descending Synthetic Aperture Radar（SAR） stereoradargrammetry has limited accuracy of topographic mapping due to the short spatial baseline（-100 km） and small intersection angle. In order to...One-sided ascending or descending Synthetic Aperture Radar（SAR） stereoradargrammetry has limited accuracy of topographic mapping due to the short spatial baseline（-100 km） and small intersection angle. In order to improve the performance and reliability of generating digital elevation model（DEM） from spaceborne SAR radargrammetry, an exploration of two-sided stereoradargrammetry from the combination of ascending and descending orbits with geometric configuration of long spatial baseline（-1000 km） was conducted in this study. The slant-range geometry between SAR sensors to the earth surface and the Doppler positioning equations were employed to establish the stereoscopic intersection model. The measurement uncertainty of two-sided radargrammetric elevation was estimated on the basis of radar parallax of homogeneous points between input SAR images. Two stereo-pairs of ALOS/PALSAR（Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar） acquisitions with the orbital separation almost 1080 km over the west Sichuan foreland basin with rolling topography in southwestern China were employed in the study to obtain the up-to-date terrain data after the 2008 Wenchuan earthquake that hit this area. Thequantitative accuracy assessment of two-sided radargrammetric DEM was performed with reference to field GPS observations. The experimental results show that the elevation accuracy reaches 5.5 m without ground control points（GCPs） used, and the accuracy is further improved to 1.5 m with only one GPS GCP used as the least constraint. The theoretical analysis and testing results demonstrate that the twosided long baseline SAR radargrammetry from the ascending and descending orbits can be a very promising technical alternative for large-area and high accuracy topographic mapping.展开更多
During geodetic monitoring with GNSS technology one of important steps is the correct processing and analysis of the measured displacements. We used the processing method of Kalman filter smoothing algorithm, which al...During geodetic monitoring with GNSS technology one of important steps is the correct processing and analysis of the measured displacements. We used the processing method of Kalman filter smoothing algorithm, which allows to evaluate not only displacements, but also the speed, acceleration, and other characteristics of the deformation model. One of the important issues is the calculation of the obser- vations weight matrix in the Kalman filter. Recurrence algorithm of Kalman filtering can calculate and specify the weights during processing. However, the weights obtained in such way do not always exactly correspond to the actual observation accuracy. We established the observations weights based on the accuracy of baseline measurements. In the presented study, we offered and investigated different models of establishing the accuracy of the baselines. The offered models and the processing of the measured displacements were tested on an experimentally geodetic GNSS network. The research results show that despite of different weight models, changing weights up to 2 times do not change Kalman filtering ac- curacy extremely. The significant improvements for Kalman filtering accuracy for baselines shorter than 10 km were not got. Therefore, for typical GNSS monitoring networks with baseline range 10-15 km, we recommend to use any kind of models. The compulsory condition for getting correct and reliable results is checking results on blunders. For baselines, which are longer than 15 km we propose to use weight model which include baseline standard deviation from network adjustment and corrections for baseline length and its accuracy.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.41472255,51178404)Open Research Fund by Sichuan Engineering Research Center for Emergency Mapping & Disaster Reduction(Program K2014B006)Fundamental Research Funds for the Central Universities(Grant Nos.SWJTU12ZT07,2682014BR014)
文摘One-sided ascending or descending Synthetic Aperture Radar（SAR） stereoradargrammetry has limited accuracy of topographic mapping due to the short spatial baseline（-100 km） and small intersection angle. In order to improve the performance and reliability of generating digital elevation model（DEM） from spaceborne SAR radargrammetry, an exploration of two-sided stereoradargrammetry from the combination of ascending and descending orbits with geometric configuration of long spatial baseline（-1000 km） was conducted in this study. The slant-range geometry between SAR sensors to the earth surface and the Doppler positioning equations were employed to establish the stereoscopic intersection model. The measurement uncertainty of two-sided radargrammetric elevation was estimated on the basis of radar parallax of homogeneous points between input SAR images. Two stereo-pairs of ALOS/PALSAR（Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar） acquisitions with the orbital separation almost 1080 km over the west Sichuan foreland basin with rolling topography in southwestern China were employed in the study to obtain the up-to-date terrain data after the 2008 Wenchuan earthquake that hit this area. Thequantitative accuracy assessment of two-sided radargrammetric DEM was performed with reference to field GPS observations. The experimental results show that the elevation accuracy reaches 5.5 m without ground control points（GCPs） used, and the accuracy is further improved to 1.5 m with only one GPS GCP used as the least constraint. The theoretical analysis and testing results demonstrate that the twosided long baseline SAR radargrammetry from the ascending and descending orbits can be a very promising technical alternative for large-area and high accuracy topographic mapping.
文摘During geodetic monitoring with GNSS technology one of important steps is the correct processing and analysis of the measured displacements. We used the processing method of Kalman filter smoothing algorithm, which allows to evaluate not only displacements, but also the speed, acceleration, and other characteristics of the deformation model. One of the important issues is the calculation of the obser- vations weight matrix in the Kalman filter. Recurrence algorithm of Kalman filtering can calculate and specify the weights during processing. However, the weights obtained in such way do not always exactly correspond to the actual observation accuracy. We established the observations weights based on the accuracy of baseline measurements. In the presented study, we offered and investigated different models of establishing the accuracy of the baselines. The offered models and the processing of the measured displacements were tested on an experimentally geodetic GNSS network. The research results show that despite of different weight models, changing weights up to 2 times do not change Kalman filtering ac- curacy extremely. The significant improvements for Kalman filtering accuracy for baselines shorter than 10 km were not got. Therefore, for typical GNSS monitoring networks with baseline range 10-15 km, we recommend to use any kind of models. The compulsory condition for getting correct and reliable results is checking results on blunders. For baselines, which are longer than 15 km we propose to use weight model which include baseline standard deviation from network adjustment and corrections for baseline length and its accuracy.
