Ground and Top of Canopy Extraction From Photon-Counting LiDAR Data Using Local Outlier Factor With Ellipse Searching Area

Abstract

The future ICESat-2 is the next generation of NASA's ICESat (Ice, Cloud and land Elevation Satellite) mission scheduled to be launched in 2018. The new photon counting LiDAR onboard ICESat-2 introduced new challenges to the estimation of forest parameters and their dynamics, the greatest being the abundant photon noise appearing in returns from the atmosphere and below the ground. To identify the potential forest signal photons, we proposed an approach by using a local outlier factor (LOF) modified with ellipse searching area. Six test data from two kinds of the photon counting LiDAR data in the USA are used to test and evaluate the performance of our algorithm. The classification results for noise and signal photons showed our approach has a good performance not only in lower noise rate with relatively flat terrain surface but also works even for a quite high noise rate environment in relatively rough terrain. The quantitative assessment indicates that the horizontal ellipse searching area gives the best result compared with the circle or vertical ellipse searching area. These results demonstrate our methods would be useful for future ICESat-2 vegetation study.

Review of Noise Filtering Algorithm for Photon Data

“…There is a significant difference between the SPL data and the ATLAS photon data, so the voxel-based spatial filtering algorithm is not necessarily applicable in the ATLAS photon data. Chen et al (2019) proposed the distance between photons by using vertical elliptical shape, horizontal elliptical shape and circular shape.…”

Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area

“…ATLAS technology is still in the airborne simulation stage, lacking evaluation criteria for noise filtering. In previous research [12,15,20], visual inspection was used as a first-step evaluation standard for photon cloud filtering. In this work, the PSODBSCAN algorithm is applied to the MATLAS data at different laser beam intensities and pointing types, and both qualitative and quantitative analyses are conducted by visual inspection using the corresponding KML file.…”

“…Namely, R denotes the ratio of signal photons that are successfully detected to all the true signal photons, P denotes the ratio of true signal photons that are correctly classified to all the detected signal photons, and F denotes the harmonic mean of recall and precision. These three indicators are calculated using the reference classification data [15,20], and they are respectively given by:…”

Potential of Forest Parameter Estimation Using Metrics from Photon Counting LiDAR Data in Howland Research Forest

“…The method we use in this study is shown in Figure 4. The method is divided into 4 parts: first, we extract the ground and canopy surface from the potential signal photons identified by the noise filtering algorithm using a local outlier factor (LOF) with an elliptical searching area [39]. Next, a co-registration technique based on moving profiling is applied between SIMPL data and G-LiHT data to correct the geolocation error.…”

“…where x and h represent the distance and height of photons, a and b represent the major and minor axis of the ellipse respectively. In this paper, we use an empirical ratio which is a:b = 6:1 [39]. Figure 5 demonstrates the distance matrix from the ellipse searching shape.…”