Abstract
Fast Fourier Transform-based Space-Time Image Velocimetry (FFT-STIV) has gained considerable attention due to its accuracy and efficiency. However, issues such as false detection of MOT and blind areas lead to significant errors in complex environments. This paper analyzes the causes of FFT-STIV gross errors and then proposes a method for validity identification and rectification of FFT-STIV results. Three evaluation indicators-symmetry, SNR, and spectral width-are introduced to filter out invalid results. Thresholds for these indicators are established based on diverse and complex datasets, enabling the elimination of all erroneous velocities while retaining 99.83% of valid velocities. The valid velocities are then combined with the distribution law of section velocity to fit the velocity curve, rectifying invalid results and velocities in blind areas. The proposed method was tested under various water levels, weather conditions, and lighting scenarios at the Panzhihua Hydrological Station. Results demonstrate that the method effectively identifies FFT-STIV results and rectifies velocities in diverse environments, outperforming FFT-STIV and achieving a mean relative error (MRE) of less than 8.832% within 150 m. Notably, at night with numerous invalid STIs at a distance, the proposed method yields an MRE of 4.383% after rectification, outperforming manual labeling.