PHOTON-COUNTING LASER ALTIMETER DATA FILTERING BASED ON HIERARCHICAL ADAPTIVE FILTER FOR FOREST SCENARIO

Abstract. Photon-counting laser altimeter data has widely used in forest surveying and mapping with the successfully launch of ICESat-2. However, it is a challenge work that extract signal information from a large amount of noise photons, especially in complex scenario like forest. In this paper, we proposed the hierarchical adaptive filter method to extract signal photons and used this method to filtering ATL03 data in forest scenario. The hierarchical adaptive filter method follows a coarse-to-fine strategy to identify noise photons step by step. The most of noise are removed by filter based on feature of local distance, firstly. Then, the modified adaptive direction filter method is used to remove the noise photons near ground surface and vegetation. Finally, the filter based on continuity of topographic is used to remove the residual noise in the atmosphere. The airborne lidar data in experiment region was used to validate the effectiveness of method. The filter result overall recall is 97.51%, the overall precision is 98.58%, and the F-score is 98.04%. The result show that the hierarchical adaptive method we proposed can extract signal photons in background information effectively and preserve vegetation photons well.