A novel spaceborne photon-counting laser altimeter denoising method based on parameter-adaptive density clustering

To tackle the challenge of denoising spaceborne photon-counting laser altimeter point clouds with uneven noise density, this study proposes a denoising method based on adaptive parameter density clustering, which utilizes numerical simulations to achieve self-adaptation of key parameters (neighborhood radius EpsEps and minimum number of points MinPtsMinPts). First, taking the directional adaptive ellipse DBSCAN (DAE-DBSCAN) as an example, photons with different background photon count rates (bckgrd_ratebckgrd_rate) are used to traverse EpsEps and MinPtsMinPts to calculate their optimal values (EpsEps and MinPtsMinPts with the highest denoising accuracy). Then, a mathematical prediction model of bckgrd_ratebckgrd_rate, EpsEps and MinPtsMinPts was established. The actual background photon count rates were introduced into the key parameter prediction model to obtain the optimal EpsEps and MinPtsMinPts. Finally, a denoising experiment was conducted using the simulated photons and the ATLAS data. The results show that the proposed method had higher accuracy than the constant parameter denoising method, with an F >0.95. Even for photons of complex mountainous terrain with a high background photon count rate, the denoising accuracy was still higher than 0.9. The proposed method improves the denoising accuracy of photons with different noise densities by adapting density clustering parameters.