期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2004
卷号：XXXV Part B3
页码：195-200
出版社：Copernicus Publications
摘要：This paper proposes a new filtering method of non-ground measurements from airborne LIDAR data through a Simultaneous AutoRegressive (SAR) analytical model and exploiting a Forward Search (FS) algorithm (Atkinson and Riani, 2000, Cerioli and Riani, 2003), a newly developed tool for robust regression analysis and robust estimation of location and shape. In SAR models, with respect to classical spatial regression models, the correlation among adjacent measured points is taken into account, by considering two quantities for the measured dataset: a coefficient of spatial interaction and a matrix of point adjacency (binary digits for regular grids or real numbers for irregular ones). FS approach allows a robust iterative estimation of SAR unknowns, starting from a subset of outlier-free LIDAR data, suitably selected. The method proceeds in its iterative computations, by extending such a subset with one or more points according to their level of agreement with the postulated surface model. In this way, worse LIDAR points are included only at the ending iterations. SAR unknowns and diagnostic statistical values are continuously estimated and monitored: an inferentially significant variation of the surface coefficients reveals as points included from now on can be classified as outliers or "non-ground" points. The method has been implemented using Matlab . language and applied either to differently simulated LIDAR datasets or really measured points, these last acquired with an Optech . ALTM 3033 system in the city of Gorizia (North-East Italy). For both kinds of datasets the proposed method has very well modeled the ground surface and detect the non-ground (outliers) LIDAR points
