Filtering Photon Cloud Data in Forested Areas Based on Elliptical Distance Parameters and Machine Learning Approach

Forests

Haiqiang Fu¹  Changcheng Wang¹  Shijuan Gao¹  Yi Li¹  Jun Zhu² 

[1] School of Geoscience and Info-Physics, Central South University, Changsha 410083, China;School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, China;

关键词: ICESat-2;    photon cloud filtering;    forested areas;    elliptical distance parameters;    noise photons;    machine learning;   

DOI  :  10.3390/f13050663

来源: DOAJ

【 摘 要 】

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method.

【 授权许可】

Unknown