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Regularized iterative reconstruction algorithms for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), like the ones based on Maximum Likelihood (ML), offer an accurate estimate of the Power Spectrum Pattern (PSP) displaced along the Perpendicular to the Line-of-Sight (PLOS) direction. The recovered PSP is considered as ‘good-fitted’ or ‘appropriate-fitted’, since the reconstruction fits correctly enough with the position and density of the objectives in the field backscattered towards the sensor. However, the correct functioning of these regularization approaches is constrained to the proper selection of the regularization parameters. Therefore, for such a purpose, this paper suggests using a criterion based on the Stein’s Unbiased Risk Estimate (SURE) strategy. SURE approximates the Mean Square Error (MSE) between the estimated and actual PSP, purely from the measured (observed) data, without the need of any knowledge about the true PSP. Consequently, the proper selection of the regularization parameters corresponds to the minimum SURE value, which guarantees having a good-fitted reconstruction. The reported experiments, performed using simulated data, address the different representative cases.

Digital elevation models of tidal flats are a most valuable data source for the water management of coastal areas and need frequent updates to account for changes in sedimentation, erosion and identification of damages in building infrastructure. This paper presents the conceptual design, the processing methodology and first results of an airborne SAR campaign conducted in July 2019 at the German North Sea coast, showing the potential for accurate monitoring of height changes at decimeter level in mudflat areas, as well as indication of vegetation cover and water flooded areas.

Synthetic Aperture Radar (SAR) is an established remote sensing technique that can robustly provide high-resolution imagery of the Earth’s surface. However, current space-borne SAR systems are limited, as a matter of principle, in achieving high azimuth resolution and a large swath width at the same time. Digital beamforming (DBF) has been identified as a key technology for resolving this limitation and provides various other advantages, such as an improved signal-to-noise ratio (SNR) or the adaptive suppression of radio interference (RFI). Airborne SAR sensors with digital beamforming capabilities are essential tools to research and validate this important technology for later implementation on a satellite. Currently, the Microwaves and Radar Institute of the German Aerospace Center (DLR) is developing a new advanced high-resolution airborne SAR system with digital beamforming capabilities, the so-called DBFSAR, which is planned to supplement its operational F-SAR system in near future. It is operating at X-band and features 12 simultaneous receive and 4 sequential transmit channels with 1.8 GHz bandwidth each, flexible DBF antenna setups and is equipped with a high-precision navigation and positioning unit. This paper aims to present the DBFSAR sensor development, including its radar front-end, its digital back-end, the foreseen DBF antenna configuration and the intended calibration strategy. To analyse the status, performance, and calibration quality of the DBFSAR system, this paper also includes some first in-flight results in interferometric and multi-channel marine configurations. They demonstrate the excellent performance of the DBFSAR system during its first flight campaigns.

Among the objectives of the upcoming space missions Tandem-L and BIOMASS, is the 3-D representation of the global forest structure via synthetic aperture radar (SAR) tomography (TomoSAR). To achieve such a goal, modern approaches suggest solving the TomoSAR inverse problems by exploiting polarimetric diversity and structural model properties of the different scattering mechanisms. This way, the related tomographic imaging problems are treated in descriptive regularization settings, applying modern non-parametric spatial spectral analysis (SSA) techniques. Nonetheless, the achievable resolution of the commonly performed SSA-based estimators highly depends on the span of the tomographic aperture; furthermore, irregular sampling and non-uniform constellations sacrifice the attainable resolution, introduce artifacts and increase ambiguity. Overcoming these drawbacks, in this paper, we address a new multi-stage iterative technique for feature-enhanced TomoSAR imaging that aggregates the virtual adaptive beamforming (VAB)-based SSA approach, with the wavelet domain thresholding (WDT) regularization framework, which we refer to as WAVAB (WDT-refined VAB). First, high resolution imagery is recovered applying the descriptive experiment design regularization (DEDR)-inspired reconstructive processing. Next, the additional resolution enhancement with suppression of artifacts is performed, via the WDT-based sparsity promoting refinement in the wavelet transform (WT) domain. Additionally, incorporation of the sum of Kronecker products (SKP) decomposition technique at the pre-processing stage, improves ground and canopy separation and allows for the utilization of different better adapted TomoSAR imaging techniques, on the ground and canopy structural components, separately. The feature enhancing capabilities of the novel robust WAVAB TomoSAR imaging technique are corroborated through the processing of airborne data of the German Aerospace Center (DLR), providing detailed volume height profiles reconstruction, as an alternative to the competing non-parametric SSA-based methods.

International audience; SAR imaging of underfoliage targets has to face a complex mixture of diverse scattering mechanisms. To characterize this complex scattering environment, nonparametric tomographic estimators are more robust to focusing artefacts but limited in resolution. Parametric tomographic estimators provide better vertical resolution but fail to adequately characterize continuously distributed volumetric scatterers such as forest canopies. To overcome these limitations, this paper addresses a new wavelet-based approach for 3D characterization of underfoliage targets. The effectiveness of this new approach is demonstrated by using L-band Multi-Baseline PolInSAR Data over Dornstetten, Germany.

[ABSTRACT]The outstanding performance capabilities of the three Satellite POLSAR sensors are well established; and in this exposition the exploitation of the fully polarimetric ALOS-PAL=PPL=SAR mode is demonstrated by implementing the NIIGATA-UNIVERSITY four-scatterer SAR image decomposition with coherency-matrix rotation proving the superior imaging capabilities of the fully polarimetric SAR modes not only for the ALOS-PALSAR L-Band and similarly to the S-Band, C-Band and X-Band. The novel fully polarimetric POL-SAR image processing techniques are then applied to natural hazard detection and subsequent disaster reduction of taiphoons with landslides, volcano eruptions with plume aftereffects & landslides, and of earthquakes with drop-slips experienced within the SE-Asian/Pacific Ring-of-Fire including next to Japan in Taiwan, the Philippines and Indonesia, promoting equatorially orbiting Single and TanDEM L-/S-/X-Band POLSAR sensor deployment.

A distributed real-time processing framework is used to implement a generic processor for linear and nonlinear tracks. The focus is on the implementation of direct backprojection (DBP) and fast factorized back-projection (FFBP) algorithms in the framework. The software is described in detail on how it is tailored to achieve a real-time computation of a SAR image using multiprocessors, multicore CPUs and GPUs. Results are validated with airborne SAR data acquired by the DLR’s F-SAR sensor.

This document provides additional information and results of the method NL-SAR described in our paper: "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising" submitted to IEEE Trans. on Geoscience and Remote Sensing [Deledalle et al., 2013]. NL-SAR is a fully automatic method for speckle reduction that handles amplitude, polarimetric and/or interferometric SAR data. It can process single look and multi-look images. The source code of the method is freely available at: http://www.math.u-bordeaux1.fr/cdeledal/nlsar.php.

The airborne F-SAR sensor, developed and operated by the DLR Microwaves and Radar Institute, provides very high resolution, fully polarimetric synthetic aperture radar (SAR) data in multiple frequency bands that are enhanced by several interferometric imaging modes. The high resolution/high bandwidth regime of operation introduces significant challenges regarding both data calibration and image formation, or focusing. This paper reviews the methods for accurate antenna characterization and the parameter extraction for SAR data processing. The antenna measurements must accurately portray interactions with the surroundings as mounted on the aircraft. The results presented concern the antenna characterization itself and illustrate how the quality of real polarimetric and interferometric SAR data is significantly improved.