A Multilevel Autoadaptive Denoising Algorithm Based on Forested Terrain Slope for ICESat-2 Photon-Counting Data

In complex mountainous terrain, the terrain slope causes the scattering of pulsed lasers, generating a lot of noise in photon cloud data (PCD) collected from forestland, which seriously affects the accurate retrieval of forest structure parameters. To address this problem, a multilevel autoadaptive denoising (MLAD) algorithm was proposed in this article. First, random noise photons were removed through the ordering points to identify the clustering structure (OPTICS) algorithm in the coarse denoising process. Second, in the fine denoising step, the circular search domain in the OPTICS algorithm was replaced with an elliptical search domain. The photons after coarse denoising were automatically divided along-track direction into several continuous segments of 100 m each. The median slope method was used to automatically calculate the slope of the forested terrain in each interval segment, so that the range of the ellipse search domain was automatically adjusted to achieve accurate denoising of PCD. Finally, the denoising results of the MLAD algorithm in three different forested terrain areas were compared with those of the difference, regression, and Gaussian adaptive nearest neighbor (DRAGANN) algorithms, and the performance of the MLAD algorithm was evaluated for both different terrain slopes and different vegetation coverages. The results indicated that compared with the DRAGANN algorithm, the MLAD algorithm has higher denoising capability in different regions. The denoising results of the MLAD algorithm exhibit slight changes with the variation in slope, and the
F
-values are around 0.96, demonstrating good robustness. The
F
-value of the MLAD algorithm mostly exceeds 0.95 in different vegetation coverages. Overall, the MLAD algorithm exhibits stronger noise identification capabilities for complex forest environments. These results can provide a reference for subsequent accurate extraction of forest structural parameters.