An Elliptical Distance Based Photon Point Cloud Filtering Method in Forest Area

Abstract

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), launched in May 2019, increased the availability of different types of spaceborne laser altimetry data. But the obtained photon point cloud, especially those for the forest area with steep terrains, contains a lot of background noise that may greatly decrease the accuracy of the extracted digital elevation model (DEM) and forest height. Therefore, removing the background noise photons mixed up with the signal photons is necessary. We proposed a method for photon point cloud filtering using the backward elliptical distance (BED). First, we used the BED to express the spatial distance of the photon point cloud. On this basis, the backward local density was derived to identify signal photons and noise photons. Then we divided the data into several segments and set a local threshold for each segment to identify signal photons and noise photons. We validated the proposed method in the forested area with steep terrains in Washington State and Spain, and compared the results with that of other filtering methods. The comparison shows that the proposed method separates signal photons and noise photons better than other methods. The comprehensive evaluation indexes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> in Spain and that of the left, center, and right channels in Washington reach 0.9892, 0.9899, 0.9905, and 0.9915, respectively. In addition, compared with the global threshold selection, the local threshold selection is more stable.