Generation of Global Backscatter Maps for Future SAR Missions Design

The generation of global backscatter maps allows for the exploitation of a priori knowledge of local synthetic aperture radar (SAR) backscatter statistics. SAR backscatter maps can be used for accurate performance prediction and for the optimization of instrument settings for present and future SAR systems. Also, many further SAR applications can benefit from the availability of backscatter maps in order to monitor the backscatter evolution in time and to investigate the radar reflectivity behaviour depending on sensor parameters and target properties. In this work, X-band backscatter maps are generated by mosaicking images acquired by the TerraSAR-X (TSM) and the TanDEM-X (TDM) missions at global scale. The correction models used for the characterization of backscatter behaviour are based on the database provided by Ulaby and are here presented for HH polarization and for any required reference incidence angle. As an example of application for future SAR missions design, a novel performance-optimized block-adaptive quantization (PO-BAQ), coming from the need of optimizing the resource allocation of the state-of-the-art quantization algorithms for SAR systems, is then considered. The methodology relies on global backscatter statistics for the generation of bitrate maps, which can provide a helpful information for performance budget definition and for optimizing resource allocation. strategie

Characterization of the Amazon Rainforest Backscatter for X-Band SAR Calibration Using TanDEM-X Data

The radiometric calibration of spaceborne SAR products plays a key role for ensuring a good performance of the whole end-to-end system and requires a precise knowledge of both the radar system and the illuminated target. The shape of the antenna pattern in elevation can be directly estimated by analyzing SAR detected images in presence of a flat backscatter profile in the slant range dimension. This is typically accomplished by acquiring SAR data over homogeneous distributed targets, under the assumption of isotropic scattering. This is the case of tropical rainforests, such as the Amazon and Congo forests, which have been established by the SAR community as well-known test sites for SAR calibration, thanks to their homogeneous and almost isotropic signature. Nevertheless, several studies using X- and C-band sensors have shown a slight dependency of the rainforest backscatter on the incidence angle, as well as on ground target properties and meteorological conditions. The aim of this work is to present a statistical characterization of radar backscatter at X-band over the Amazon rainforest using TanDEM-X data, and to provide insights on how to best utilize radar backscatter data in this region for SAR calibration and modeling purposes

Assessment of Image Quality of Waveform-Encoded Synthetic Aperture Radar Using Real Satellite Data

Synthetic aperture radar (SAR) remote sensing is very attractive for the systematic observation of dynamic processes on the Earth’s surface since it allows high resolution imaging independently of weather conditions and sunlight illumination. Waveform-encoded SAR is a novel SAR concept based on pulse-to-pulse variation of the transmitted waveform that allows focusing the nadir echo and the range ambiguities and suppressing them through a multi-focus post-processing. However, the assessment of the ambiguity suppression performance for such a system is not trivial, as the processing involves a (non-linear) thresholding and blanking approach. This work proposes a novel methodology, which exploits real TerraSAR-X data to accurately simulate the effect of the range ambiguity on the useful signal and allows for a quantitative assessment of the image quality of a waveform-encoded SAR. The analysis considers different waveform variation schemes (e.g., up- and down-chirps, cyclicallyshifted chirps) and a contrast-minimization technique for threshold selection, whose performance is compared to the best achievable one (i.e., optimum threshold). The results of this work further highlight the potentialities of the waveform-encoded SAR concept and also allow accounting for its ambiguity suppression capability in the design of a SAR system

Optimizing Φ-Net Using TanDEM-X Bistatic Data for High-Resolution DEM Generation

Phi-Net is a deep residual learning architecture and is currently one of the most accurate state-of-the-art approaches for Synthetic Aperture Radar interferometric (InSAR) phase filtering and coherence estimation. The network has proven to outperform classical denoising strategies concerning the estimation of InSAR parameters and therefore is very suitable for the generation of high-resolution Digital Elevation Models (DEMs). However, some limitations are shown over critical areas characterized by low signal-to-noise ratio (SNR) or by geometric distortions, i.e. shadow and layover. There, Phi-Net wrongly reconstructs the InSAR phase and inserts artefacts, thus leading to an unreliable input for typical phase unwrapping algorithms. Moreover, being trained with synthetic data only, there is still potential for an optimization which is closely related to the patterns that can be found in real InSAR data. In this paper we propose a preliminary analysis which exploits TanDEM-X bistatic data in order to optimize and fine tune Phi-Net. In particular, we address the problem of removing artefacts in the filtered InSAR phase and improving the estimation performance, which would allow for the generation of a more accurate and reliable unwrapped phase and higher-precision DEMs

Waveform-Encoded Synthetic Aperture Radar: Image Quality Assessment Using Satellite Data

Synthetic aperture radar (SAR) remote sensing is very attractive for the systematic observation of dynamic processes on the Earth’s surface since it allows high resolution imaging independent of weather conditions and sunlight illumination. Waveform-encoded SAR is a novel SAR concept based on pulse-to-pulse variation of the transmitted waveform that allows focusing the nadir echo and the range ambiguities and suppressing them through a multi-focus post-processing. However, the assessment of the ambiguity suppression performance for such a system is not trivial, as the processing involves a non-linear thresholding and blanking approach. This work proposes a novel methodology, which exploits real TerraSAR-X data to accurately simulate the effect of the range ambiguity on the useful signal and allows for a quantitative assessment of the image quality of a waveform-encoded SAR. The analysis considers different waveform variation schemes (namely up- and down-chirps and cyclically shifted chirps) and a contrast-minimization technique for threshold selection, whose performance is compared to the best achievable one (i.e., the optimum threshold). The results of this work further highlight the potentialities of the waveform-encoded SAR concept and allow accounting for its ambiguity suppression capability in the design of a SAR system

Range Ambiguity Smearing and Suppression: Comparison of Different Azimuth Phase Codes and Opportunities from Multi-Focus Post-Processing

Synthetic Aperture Radar (SAR) remote sensing has become very attractive in Earth observation applications, since it provides high resolution imaging independent of weather conditions and sunlight illumination. However, the design of a SAR system is still constrained by nadir returns and range ambiguities, which can strongly corrupt the quality of the SAR image, if not avoided. Recently, a novel SAR concept, named waveform-encoded SAR, based on pulse-to-pulse variation of the transmitted waveform and on a dual/multi-focus post-processing, has been proposed in order to cope with these ambiguities. While some implementation issues related to waveform variation are still under analysis, this work investigates a simpler variant, where waveform encoding is replaced by azimuth phase encoding and multi-focus post-processing is always performed. In particular, the range ambiguity smearing and suppression capability of four different azimuth phase codes is investigated as a function of the Pulse Repetition Frequency (PRF). Some investigations based on real TerraSAR-X data simulations are performed to prove the effectiveness of the proposed technique in terms of ambiguity suppression and smearing

Artifact detection in SAR images with AI methods

The increasing number of Earth observation data necessitates for advanced automated evaluation. Autoencoders (AE), which are deep neural networks, have been successfully applied to change detection on optical images. Here, we present an investigation of the applicability of three different convolutional AE methods for change detection on time series of SAR images. During the evaluation, the so-called joint AE approach is proved to be more precise and less sensitive to changes in brightness, thus designating less false positives. Moreover, the joint AE method indicates three noticeable and conspicuous regions

Characterization of Tropical Rainforest for X-Band Spaceborne SAR Calibration Using TanDEM-X Data

The radiometric calibration of spaceborne SAR products plays a key role for assuring a good performance of the whole end-to-end system and requires a precise knowledge of both the radar system and the illuminated target. For example, the shape of the antenna pattern in elevation can be directly estimated by analysing SAR detected images in presence of a homogeneous backscatter profile in the slant range dimension. To this aim, tropical rainforests have been established by the SAR community as well-known calibration sites for performing such an activity. Here, according to the hypothesis of isotropic scattering, the backscattering coefficient in terms of unit area perpendicular to the antenna, called gamma nought, is assumed to remain constant with respect to the incidence angle. Nevertheless, several studies using X- and C-band sensors have shown a slight dependency of the backscatter on the incidence angle, as well as on ground target properties and meteorological conditions. In this work, we present a detailed statistical characterization of radar backscatter at X-band over the Amazon rainforest using TanDEM-X high-resolution data, and we provide insights on how to best utilize radar backscatter data in this region for SAR calibration and modelling purposes. In particular, we concentrate on the dependence of the Amazon rainforest backscatter on the day-time of acquisition, which is directly related to the orbit direction of TanDEM-X. Furthermore, we analyse the seasonal variation of the radar backscatter, as well as possible variations with respect to different latitudes, characterized by different climate conditions and biomes. Finally, we provide a series of backscatter models for different scenarios, which can be used, e.g., for X-band spaceborne SAR system design and theoretical modelling