Signal Processing Options for High Resolution SAR Tomography of Natural Scenarios

Abstract

Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.

Three- and Four-Dimensional Topographic Measurement and Validation

“…A new processing method was proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. The analysis was validated by results from numerical simulations, as well as from real airborne data from the ESA campaign AlpTomoSAR [71].…”

Tomographic Performance of Multi-Static Radar Formations: Theory and Simulations

“…In this section, we present and compare several tomograms obtained from simulations when platform and target distributions are both in 2D as shown in Figures 9 and 11, respectively. The 2D raw data and the 2D tomographic images are generated using (18) and (19), respectively. No windowing is applied.…”

“…Several works on TomoSAR have been published since the first demonstrations and signal data models appeared in the literature [6,7,[9][10][11][12][13]. Over the past two decades, SAR tomography has been applied with increasingly promising results to forests [12][13][14][15][16][17][18][19][20][21][22], croplands [23], urban environment [24][25][26][27][28][29], and, more recently, ice [30] and snow [31][32][33][34][35]. All these natural media share a volumetric structure causing multiple scatterers to lie within the same radar resolution cell, which TomoSAR can identify and disentangle, providing opportunities for resolving the internal components of the media.…”

Deriving Digital Surface Models from Geocoded SAR Images and Back-Projection Tomography

“…We simplify notation by assuming that the offsets ∆e i and ∆n i are identical for all samples in γ k,i . This assumption is a valid approximation for data acquired with linear trajectories and stripmap mode, since in that case we can assume that in the image domain (range, azimuth and elevation) are perpendicular to each other, and a common elevation dimension exists [16]. The tracks are assumed to be parallel to each other in case of using data sets recorded in multiple passes.…”

“…SAR image formation in time domain requires a comparatively high computational complexity and offers few benefits for data recorded in stable illumination conditions, like spaceborne SAR. The small angular diversity of a tomographic spaceborne SAR data acquisition does not require back-projection for 3D reconstruction [15], [16]. These reasons explain the scarce use of map geometry for 3D image formation purposes.…”