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

Abstract

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters.

An Improved Particle Swarm Optimisation Method for Performance Evaluation of Instructors

“…Regarding the various indicators and factors in the assessment system, such as how to formulate specific indicators for the instructor's assessment, how to make the indicators more scientific and objective, and how to stimulate the enthusiasm of the instructor to better participate in the assessment influence in the system [26,27]. Many factors outside the appraisal system will also have an impact on the performance of the counselor, thereby affecting the effectiveness of the counselor's performance appraisal [28,29]. erefore, in the college system, how to manage the performance of counselors more effectively is not only a problem that the student work department should think about but also a problem that the management of other departments of the university should think about together.…”

Assessing the Performance of ICESat-2/ATLAS Multi-Channel Photon Data for Estimating Ground Topography in Forested Terrain

“…The ICESat-2 mission produces along-track ground topography in forested terrain that includes telemetry data (ATL00), reformatted telemetry (ATL01), science unit converted telemetry (ATL02), global geolocated photon data (ATL03), land vegetation elevation (ATL08), and a land/canopy grid (ATL18) [37][38][39][40][41]. ATL00, ATL01, and ATL02 are original photon data sets without scientific algorithms.…”

“…A diagram of ICESat-2 for estimating ground topography in forested terrain is illustrated in Figure 2. The forward orientation (sc_orient=1) corresponds to ATLAS traveling along the +x direction in the ATLAS instrument reference frame [41][42][43]. The ATLAS signal photon shown in the yellow square represents the photons detected from the gt3r laser channel.…”

“…Due to the influence of sunlight as well as atmospheric and system noise, a large number of noise photons are present in the ATLAS data, which seriously reduce the ground elevation measurement accuracy. In order to improve the estimation accuracy of ATLAS photon data, NASA proposed a Differential, Regressive, and Gaussian Adaptive Nearest Neighbor (DRAGANN) method and ATL08 data classified algorithm to filter out noise photon data and classify ground photons [41][42][43]. In order to explore the estimation accuracy of forested terrain from ATLAS data, this contribution chose to associate the ATL08 classified label with the ATL03 photon data and used the ground signal photons flag mentioned in ATL08 as ground photons to establish an ATLAS-based DTM (Table 2) [37].…”

A Fast and Robust Algorithm with Reinforcement Learning for Large UAV Cluster Mission Planning

“…We compare our method with two effective heuristic optimization algorithms: genetic algorithm (GA) and particle swarm optimization algorithm (PSO) [39,40]. The main parameter settings of GA and PSO are shown in Tables 2 and 3.…”