Maximum‐likelihood‐based approach for single‐pass synthetic aperture radar tomography over urban areas

Abstract

Synthetic aperture radar (SAR) tomography (TomoSAR) is one of the key techniques in remote sensing for a sophisticated, three-dimensional (3D) analysis of complex scenes such as forests or urban areas. During recent years, much progress has been made in order to deal with unfavourable data conditions and to further enhance the achievable resolutions. Since TomoSAR has essentially be seen as a spectral estimation problem in the context of array signal processing, most of the methods investigated until now rely on a relatively large tomographic aperture and a relatively high number of receiving antennas in order to be able to separately reconstruct scatterers below the Rayleigh resolution limit. In contrast to that, this study proposes a maximum-likelihood-based approach for tomographic SAR inversion, which provides a better height resolution than conventional array signal processing approaches even if the number of antennas or the overall baseline size is limited. The algorithm is tested on real multi-baseline SAR data acquired over an urban area by an airborne single-pass fourantenna system operating in the millimetre-wave domain. First results show the feasibility of the method both with respect to 3D scatterer reconstruction and 3D SAR focusing.

A Unified Algorithm for Channel Imbalance and Antenna Phase Center Position Calibration of a Single-Pass Multi-Baseline TomoSAR System

“…As a multi-baseline system, the array InSAR system can obtain 3-D images of the observed scene in a single-pass platform. The Rayleigh resolution [5] in the height direction of the array InSAR system can be derived approximately as…”

“…In recent years, synthetic aperture radar (SAR) tomography (TomoSAR) has become a popular research topic due to its 3-D imaging capability [1][2][3][4][5][6]. TomoSAR has been successfully applied in many application contexts, such as forestry [7,8], 3D urban reconstruction [9,10], and glaciers [11].…”

Generating point clouds of forested areas from airborne millimeter wave InSAR data

“…The second one is a two-step approach consisting of a layover separation step by a maximum-likelihood-based SAR tomography algorithm [14], followed by a fusion of the resulting 3D point clouds via voxel space representation. Both approaches use a 2D array of complex covariance matrices (one covariance matrix per pixel) as input data.…”

The InSAeS4 Airborne X-Band Interferometric SAR System: A First Assessment on Its Imaging and Topographic Mapping Capabilities

“…Examples of such systems are the C-/X-Band F-SAR [12], the X-Band PAMIR [13], the X-/Ku-Band RAMSES [14], the X-Band SETHI [15], the X-Band OrbiSAR [9] and the Ka-Band MEMPHIS [16][17][18][19][20]. As a matter of fact, in these systems the InSAR baseline can be as small as required by the geometrical constraints imposed by small airborne platforms.…”