%0 Dataset
%T Scientific Data from Drone Ground-Penetrating Radar Detection in Yan'an Region
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/501ee6d4-d712-4dac-aa73-d93907baa861
%W NCDC
%R 10.12072/ncdc.loess.db7344.2026
%A feng xuan
%K GPR;drown;matlab
%X This study employed a drone-mounted 100 MHz ground-penetrating radar (GPR) to conduct non‑destructive detection of the subsurface structure of loess landslides, aiming to identify underground features such as sinkholes and fractures. Two test‑flight experiments were carried out: Experiment 1 involved large‑area rapid detection, while Experiment 2 focused on medium‑range detection. A data‑processing workflow was designed for the raw data collected during the flights, which included background removal, gain adjustment, band‑pass filtering, median filtering, and adaptive filtering. Comparison of the images before and after processing shows that the coupling between the transmitting and receiving antennas was effectively suppressed, the signal energy from below the ground surface was well compensated, and random noise interference in the raw data was also significantly reduced.