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.
Highlights
Synthetic Aperture Radar Tomography (TomoSAR) is generally referred to as an active microwave imaging technology to resolve the illuminated targets in the three dimensional (3D) space using multiple SAR acquisitions [1]
We have shown that the popular vector-to-vector 1D processing approach, largely assumed in the literature and declared in many different forms using spectral estimation techniques, is only applicable for cases where residual range migration does not exceed range resolution, that is the case where data are characterized by coarse vertical resolution
Both these topics were addressed in the second part of this paper, where we proposed a processing algorithm based on sub-sampled defocusing and refocusing, consisting of the generation of sub-sampled raw data from available Single Look Complex (SLC) data, to be focused afterward in the three-dimensional space at a largely reduced computational cost
Summary
Synthetic Aperture Radar Tomography (TomoSAR) is generally referred to as an active microwave imaging technology to resolve the illuminated targets in the three dimensional (3D) space using multiple SAR acquisitions [1]. TomoSAR imaging is based on the collection of multiple flight trajectories to form a 2D synthetic aperture This allows focusing the received signal in the range/azimuth plane, as in conventional 2D SAR imaging, and in elevation, resulting in the possibility to resolve the targets in three-dimensions (3D). The downside is that penetration entails that the received signal is determined by a variety of different scattering mechanisms, including surface scattering, volume scattering, scattering from internal layers, as well as multiple-scattering phenomena, which hinders physical interpretation based on 2D imaging In this context, the use of SAR tomography has provided researchers with the possibility to directly see the structure of electromagnetic reflections in the interior of the illuminated media, resulting in a significant advancement of the understanding of the way Radar waves interact with natural media.
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