Yanghai Yu
Wuhan University
6 Papers
Yanghai Yu is an academic researcher from Wuhan University. The author has contributed to research in topics: Computer science & Synthetic aperture radar. The author has an hindex of 3, co-authored 3 publications.
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Papers
Measuring precursory movements of the recent Xinmo landslide in Mao County, China with Sentinel-1 and ALOS-2 PALSAR-2 datasets
TL;DR: In this article, the authors used high-resolution TerraSAR-X intensity images to evaluate the landslide disaster and delineate the sliding area, using the conventional differential InSAR (DInSAR) method and advanced time series inSAR analysis.
105
GPU accelerated interferometric SAR processing for Sentinel-1 TOPS data
TL;DR: A GPU accelerated S-1 InSAR processing method implemented on a personal desktop, which reduces the computation time from 1415.32s to 8.59s, and develops a novel GPU-based parallel coherence estimation algorithm in ESD and coherent estimation modules.
27
Assessment of underlying topography and forest height inversion based on TomoSAR methods
TL;DR: In this article , three typical algorithms, namely, Capon, Multiple Signal Classification (MUSIC), and Compressed Sensing (CS), were adopted to evaluate the performance in forest height and underlying topography inversion.
6
Large-Scale Forest Height Mapping in the Northeastern U.S. using L-Band Spaceborne Repeat-Pass SAR Interferometry and GEDI LiDAR Data
Yanghai Yu,Yang Lei,Paul Siqueira +2 more
- 16 Jul 2023
TL;DR: Large-scale forest height mapping using L-band SAR interferometry and GEDI LiDAR data demonstrates a promising fusion prototype with high accuracy and spatial resolution.
2
Tomographic Calibration and Processing for Repeat-Pass Bistatic Airborne SAR: A Case Study on New ESA Tomosense L-Band Data
Yanghai Yu,Stefano Tebaldini,Mauro Mariotti d'Alessandro,Yang Lei,Mingsheng Liao +4 more
- 16 Jul 2023
TL;DR: Tomographic calibration and processing for repeat-pass bistatic airborne SAR data presents challenges due to navigational data uncertainties and clock mismatches. A dedicated calibration approach is developed to compensate for these disturbances, significantly improving the interferometric and tomographic performances on TomoSense L-band data.