Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed.

This article addresses a novel methodology for the utilization of Field Programmable Gate Array (FPGA) accelerators in on-board Synthetic Aperture Radar (SAR) processing routines. The methodology consists of using High-Level Synthesis (HLS) to create Intellectual property (IP) blocks and using the Reusable Integration Framework for FPGA Accelerators (RIFFA) to develop a Peripheral Component Interconnect express (PCIe) interface between the Central Processing Unit (CPU) and the FPGA, attaining transfer rates up to 15.7 GB/s. HLS and RIFFA reduce development time (between fivefold and tenfold) by using high-level programming languages (e.g., C/C++); moreover, HLS provides optimizations like pipeline, cyclic partition, and unroll. The proposed schematic also has the advantage of being highly flexible and scalable since the IPs can be exchanged to perform different processing routines, and since RIFFA allows employing up to five FPGAs, multiple IPs can be implemented in each FPGA. Since Fast Fourier Transform (FFT) is one of the main functions in SAR processing, we present a FPGA accelerator in charge of the reordering stage of VEC-FFT (an optimized version of FFT) as a proof of concept. Results are retrieved in reversed bit order, and the conventional reordering function may consume more than half of the total clock cycles. Next, to demonstrate flexibility, an IP for matrix transposition is implemented, another computationally expensive process in SAR due to memory access.

Numerous ancient cultures in Mexico (e.g., Maya, Zapotec, Olmec, etc.) disappeared, leaving behind their legacy. These cultures inherited temples and archaeological remains, discovered in some cases by chance or through scientific expeditions. Most of the discovered structures were covered by vegetation layers, which made them very difficult to identify. By instance, Mexico’s south-east, where the Mayan culture settled, is known for being a densely forested area. The tropical forest ‘Selva Lacandona’ is located in this region. Consequently, this work suggests using synthetic aperture radar (SAR) tomography (TomoSAR), as a technique for searching structures under the forest canopy. TomoSAR retrieves three-dimensional imagery from illuminated scenes. When an adequate wavelength is chosen (i.e., L-, P-band), TomoSAR detects the several vegetation layers in forested areas and the topography beneath. Furthermore, TomoSAR is also capable of locating structures hidden under the forest canopy, which could be the case of ancient temples or archaeological vestiges.