Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights

Abstract

Interferometric synthetic aperture radar (INSAR) and laser altimeter (LIDAR) systems are both widely used for mapping topography. INSAR can map extended areas but accuracies are limited over vegetated regions, primarily because the observations are not measurements of true surface topography. The measurements correspond to a height above the true surface that depends on both the sensor and the vegetation. Conversely, topography from LIDAR is very accurate, but coverage is limited to smaller regions. The authors demonstrate how these technologies can be used synergistically. First, the authors determine surface elevations and vegetation heights from dual-baseline INSAR data by inverting an INSAR scattering model. The authors then combine sparse LIDAR observations with the INSAR inversion results to improve the estimates of ground elevations and vegetation heights. This is accomplished via a multiresolution Kalman Filter that provides both the estimates and a measure of their uncertainty at each location. Combining data from the two sensors provides estimates that are more accurate than those obtained from INSAR alone yet have dense, extensive coverage, which is difficult to obtain with LIDAR. Contributions of this work include (1) combining physical modeling with multiscale estimation to accommodate nonlinear measurement-state relationships and (2) improving estimates of ground elevations and vegetation heights for remote sensing applications.