The space-time image velocimetry technique harnesses the natural features of river surfaces for analysis. By examining the predominant texture direction in the generated space-time images, it calculates the one-dimensional time-averaged flow velocity of the river surface, factoring in physical transformation relationships, captured video parameters, and the tangent of the texture inclination angle. In view of the problem that the accuracy of the spatiotemporal image texture inclination angle detection is greatly affected by noise interference in practical applications, this paper proposes to use an improved homomorphic filter to enhance the texture features of the river surface image, and adopts a frequency domain filter integrated with adaptive histogram equalization to denoise the spatiotemporal image. Subsequently, the Radon transform is deployed to pinpoint the texture′s angular direction. Through simulated texture image experiments and on-site river experiments under high and low flow conditions, the effectiveness of the improved method proposed in this paper is verified. The findings reveal that, for standard simulated texture visuals, the Radon transform′s angle detection holds a relative error of less than 0.03%. In on-site river laden with interference, the relative errors between the Radon transform-based spatiotemporal image texture angle detections and manual observations are less than 1.56% and 1.80% under low and high flow conditions, respectively. The experiment indicates that the Radon transform method is feasible and has higher accuracy compared to other texture angle detection algorithms.