Synthetic Aperture Radar (SAR) images exhibit the presence of the so-called speckle, often considered noise, resulting from the fluctuations of the elemental scatterers present in a given resolution cell. Speckle hinders the interpretation of images and several other applications. In this regard, training a neural network to despeckle requires the presence of matched ground truth (clean images), which doesn’t exist for SAR imagery. Recent training strategies attempt to solve the issue of lack of training data (via self-supervised training) by exploiting the usage of several acquisitions (temporal diversity), the spatial diversity of image patches, or by utilizing the real/imagery information of the Single-Look Complex (SLC) SAR data. However, the information present in the spectral domain has not yet been explored to train a self-supervised network. This study presents a new way of obtaining training data based on the sublook processing of SAR data to train a self-supervised despeckling network. Our proposed approach is tested using TerraSAR-X data, with experimental results reflecting the validity of our novel training method, paving the way for a new self-supervised strategy for training neural networks for despeckling.

SAR image compression is essential for managing the large amounts of data generated by Synthetic Aperture Radar (SAR) systems, ensuring efficient storage, transmission and processing without compromising essential information. Various techniques, including wavelet-based methods and predictive coding, are commonly used to achieve an optimal balance between compression ratio and image quality. Autoencoders within a deep learning framework have been successfully applied to SAR image compression; however, the simultaneous challenge of compression and speckle reduction remains unsolved due to the lack of ground truth. This study addresses this gap by proposing a self-supervised framework for SAR speckle reduction and extending its application to the joint problem of SAR image compression. The developed network learns a representation of SAR data that not only facilitates effective speckle reduction, but also enables image compression. We compare our method with state-of-the-art despeckling and compression algorithms and show that we can perform both tasks together with excellent performance.

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft's limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

The GeoWAM project aims to achieve high-resolution and highly-accurate digital elevation models (DEM) to monitor the evolution of tidal flats over the German North Sea. To achieve this, the dual-frequency/dual-baseline (DFDB) DEM generation approach, recently developed by DLR, was further expanded to include L-band data. Based in the experience obtained in the 2019 GeoWAM campaign with X- and S-band only, the addition of L-band for the 2020 campaign helps to deal with the decorrelation found in repeat-pass data and potentially allows the creation of new merged products. For the computation of phase and coherence, a new local fringe frequency approach was included to improve the final generated DEMs.

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

This paper presents first results for an automatic interpretation of SAR images of sea ice acquired in the Davis Strait off the coast of Baffin Island in 2019. While the study provides multi-frequency and interferometric data collected by the DLR F-SAR airborne SAR sensor, we focus on the analysis of C-band as one of the most commonly used frequencies for sea ice monitoring. We apply an iterative version of polarimetric k-Means which allows to work on the local variance-covariance matrices directly. The obtained clusters show very distinct polarimetric as well as topological properties, which indicates that they are closely related to different sea ice types.

Speckle is inherent to all coherent imaging systems and affects SAR imagery in the form of strong intensity variations in pixels with similar backscattering coefficient, difficulting the interpretation of SAR data. In the context of the Beltrami filter, a polarimetric distance is utilized as part of a region growing algorithm to find and then average similar covariance matrices within a central window using an iterative scheme. The Beltrami filter has shown good results using a computationally expensive geodesic distance that takes into account the Hermitian positive definite nature of the polarimetric covariance matrices. The flexible nature of the Beltrami distance allows for the use of any polarimetric distance, allowing the study on the utilization of less computationally complex distances and their impact on speckle reduction. In this paper, an analysis on the effect of some of the commonly utilized polarimetric synthetic aperture radar (PolSAR) distances measures within the Beltrami despeckling filter will be presented.

Modern approaches for resolution enhancement (RE) and superresolution (SR) of coherent remote sensing (RS) imagery suggest to exploit the sparsity of the desired image representations in some appropriately chosen overcomplete dictionaries and treat the related RE/SR imaging inverse problems in descriptive settings imposing some structured regularization constraints. However, such approaches are not properly adapted to the SR recovery of the speckle-corrupted low resolution (LR) coherent radar imagery with preservation of salient image features. In this letter, we address a new multistage iterative SR technique for feature-enhanced radar/fractional synthetic aperture radar computational imaging. First, the despeckled high-resolution image is recovered from the LR speckle-corrupted radar image applying the descriptive-experiment-design-regularization-based reconstructive processing. Next, the multistage RE is consequently performed in each nested refined SR frame via the iterative reconstruction of the upscaled radar images, followed by the discrete-wavelet-transform-based sparsity-promoting denoising with guaranteed consistency preservation in each resolution frame.

The challenge of this study is to develop a new approach for multi-stage feature enhanced recovery of remote sensing (RS) imagery. The approach is based on modeling the spatial spectrum pattern (SSP) reflectivity map as a superposition of different image structures, i.e., edges, smooth and homogeneous texture zones. The latter usually manifest sparsity properties in some specific component dictionaries. The innovative proposition relates to incorporating into the initial descriptive experiment design regularization (DEDR) framework two additional regularization modalities: (i) the compressive sensing (CS) inspired convergence guaranteed regularizing projections onto convex solution sets (POCS) and (ii) the adaptive sparsity preserving despeckling level that performs the dictionary-based restoration (DBR) of the image features represented in the employed Haar wavelet dictionary basis. Algorithmically, the DBR processing is implemented as the shrinkage-type iterative CS technique adaptively incorporated into the overall multi-stage iterative DEDR-DBR method.