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Your search keyword '"Baseline model"' showing total 3 results
Start Over Descriptor "Baseline model" Journal remote sensing
3 results on '"Baseline model"'

1. Baseline Calibration of L-Band Spaceborne Bistatic SAR TwinSAR-L for DEM Generation.

Author

Mou, Jingwen, Wang, Yu, Hong, Jun, Wang, Yachao, and Wang, Aichun

Subjects
*SYNTHETIC aperture radar, *DIGITAL elevation models, *RELATIVE motion, *GEOMETRIC modeling
Abstract

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Baseline Calibration of L-Band Spaceborne Bistatic SAR TwinSAR-L for DEM Generation

Author

Jingwen Mou, Yu Wang, Jun Hong, Yachao Wang, and Aichun Wang

Subjects
synthetic aperture radar (SAR), bistatic SAR, TwinSAR-L, baseline model, baseline calibration, digital elevation model (DEM), Science
Abstract

The Terrain Wide-swath Interferometric L-band Synthetic Aperture Radar (TwinSAR-L) mission is a spaceborne bistatic synthetic aperture radar (SAR) mission to derive a high-quality global digital elevation model (DEM). The prerequisite of the high-accuracy DEM is knowing the interferometric baseline with high precision. The challenging problem is that the baseline of the bistatic system is highly dynamic due to the fast relative motion between the two satellites. In this paper, a pixel-related baseline model based on the geometrical shift is proposed to accurately reflect the position change of satellites. The baseline error is then calibrated using height gradient information and a small number of point targets with a slight incidence angle difference, eliminating the need for low-frequency corner reflectors and avoiding the difficulty of selecting a calibration site. The proposed method has been successfully exploited during the initial Commissioning Phase of TwinSAR-L, demonstrating its effectiveness in evaluating the precise baseline and supporting the generation of high-precision DEM.

Published
2023
Full Text
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3. Automatic Mapping of Landslides by the ResU-Net

Author

Mengfei Wei, Chao Ma, Chong Xu, Wentao Yang, and Wenwen Qi

Subjects
010504 meteorology & atmospheric sciences, Floodplain, Disaster mitigation, Science, 0211 other engineering and technologies, 02 engineering and technology, Hazard analysis, 01 natural sciences, remote sensing, Baseline (configuration management), Image resolution, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, business.industry, Deep learning, Baseline model, Landslide, deep learning models, regional landslide mapping, General Earth and Planetary Sciences, Artificial intelligence, business, Cartography, Geology
Abstract

Massive landslides over large regions can be triggered by heavy rainfalls or major seismic events. Mapping regional landslides quickly is important for disaster mitigation. In recent years, deep learning methods have been successfully applied in many fields, including landslide automatic identification. In this work, we proposed a deep learning approach, the ResU-Net, to map regional landslides automatically. This method and a baseline model (U-Net) were collectively tested in Tianshui city, Gansu province, where a heavy rainfall triggered more than 10,000 landslides in July 2013. All models were performed on a 3-band (near infrared, red, and green) GeoEye-1 image with a spatial resolution of 0.5 m. At such a fine spatial resolution, the study area is spatially heterogeneous. The tested study area is 128 km2, 80% of which was used to train models and the remaining 20% was used to validate accuracy of the models. This proposed ResU-Net achieved higher accuracy than the baseline U-Net model in this mountain region, where F1 improved by 0.09. Compared with the U-Net model, this proposed model (ResU-Net) performs better in discriminating landslides from bare floodplains along river valleys and unplanted terraces. By incorporating environmental information, this ResU-Net may also be applied to other landslide mapping, such as landslide susceptibility and hazard assessment.

Published
2020

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