Segments-based progressive TIN densification filter for DTM generation from airborne LIDAR data

Airborne light detection and ranging( LIDAR) has revolutionized conventional methods for digital terrain models( DTMs) acquisition. Ground filtering for airborne LIDAR is one of the core steps taken to obtain a high quality DTM. This paper presents a segments-based progressive TIN( triangulated irregular network) densification( SPTD) filter that can automatically separate ground points from non-ground points. The SPTD method is composed of two key steps: point cloud segmentation and clustering by iterative judgement. The clustering method uses the dual distance to obtain a set of seed points as a coarse spatial clustering process. Then the rest of the valid point clouds are classified iteratively. Finally,the datasets provided by ISPRS are utilized to test the filtering performance.In comparison with the commercial software Terra Solid,the experimental results show that the SPTD method in this paper can avoid single threshold restrictions. The expected accuracy of ground point determination is capable of producing reliable DTMs in the discontinuous areas.

FrFT-CSWSF: Estimating cross-range velocities of ground moving targets using multistatic synthetic aperture radar

Estimating cross-range velocity is a challenging task for space-borne synthetic aperture radar（SAR）, which is important for ground moving target indication（GMTI）. Because the velocity of a target is very small compared with that of the satellite, it is difficult to correctly estimate it using a conventional monostatic platform algorithm. To overcome this problem, a novel method employing multistatic SAR is presented in this letter. The proposed hybrid method, which is based on an extended space-time model（ESTIM） of the azimuth signal, has two steps： first, a set of finite impulse response（FIR） filter banks based on a fractional Fourier transform（FrFT） is used to separate multiple targets within a range gate; second, a cross-correlation spectrum weighted subspace fitting（CSWSF） algorithm is applied to each of the separated signals in order to estimate their respective parameters. As verified through computer simulation with the constellations of Cartwheel, Pendulum and Helix, this proposed time-frequency-subspace method effectively improves the estimation precision of the cross-range velocities of multiple targets.