Synthetic Aperture Radar Amplitude Data Research Articles

Tracking tsunami propagation and Island’s collapse after the Hunga Tonga Hunga Ha’apai 2022 volcanic eruption from multi-space observations

The quantity and accuracy of satellite-geodetic measurements have increased over time, revolutionizing the monitoring of tectonic processes. Global Navigation Satellite System (GNSS) and satellite radar signals provide observations beyond ground deformation, including how earthquake and tsunami processes affect variations in the ionosphere. Here, we study the Hunga Tonga Hunga Ha’apai (HTHH) volcanic eruption 2022 and its associated tsunami propagation with the analysis GNSS derived Total Electron Content (TEC), Synthetic Aperture Radar (SAR) Sentinel-1 data, complemented with tide gauge observations. We utilize GNSS sites data within a ~ 5000 km radius from the volcanic eruption for estimating the ionospheric perturbation as Vertical TEC. We give evidence on the detection of acoustic gravity, internal gravity, and atmospheric Lamb waves signatures in the TEC perturbation. In particular, the internal gravity waves that concentrated in the southwest of Tonga, directly correlates with the observed tsunami propagation direction as accounted by the tide gauge measurements. However, the acoustic gravity wave signature in the TEC is dominant in the north direction suggesting a surface deformation, which could be verified using Sentinel-1A SAR amplitude data. The analysis presented herein shows that within 5 h of the volcanic eruption, the central part of the HTHH island landscape disappeared with the biggest explosion. The unprecedented detail resolved by integrating satellite data yields previously unknown details of the deformation of the 2022 HTHH volcano eruption.

Scheme for Characterizing Clutter Statistics in SAR Amplitude Images by Combining Two Parametric Models

Accurate knowledge of the statistical properties of synthetic aperture radar (SAR) amplitude data is essential for the processing and interpretation of SAR images. During the last few decades, parametric models have been extensively investigated for use in characterizing the statistics of SAR amplitude data. Among them, the generalized gamma distribution ( $\text{G}\Gamma \text{D}$ ) has been widely applied in many fields of SAR image processing, as it has been demonstrated to be appropriate for describing the statistical behaviors of both SAR land and sea clutter. However, the $\text{G}\Gamma \text{D}$ is not effective when the third-order sample log-cumulant of SAR data is close to zero as it cannot match the distribution of actual data. In this case, the recently proposed ${\mathcal{ G}}_{\textrm {AO}} $ model shows good performance for fitting the distribution of the actual SAR data. In this paper, we theoretically derive the analytical conditions of the ${\mathcal{ G}}_{\textrm {AO}} $ model applicability. Based on the derivations, a scheme describing the statistical behaviors of SAR pixel amplitudes is given by combining the $\text{G}\Gamma \text{D}$ and the ${\mathcal{ G}}_{\textrm {AO}} $ model. Experiments performed on C-band RADARSAT-2 and L-band ALOS-PALSAR SAR data verify the effectiveness of the proposed scheme. The usefulness of the ${\mathcal{ G}}_{\textrm {AO}} $ model for extremely heterogeneous land clutter, for example, from urban regions, is also demonstrated.

Time-Series 3-D Mining-Induced Large Displacement Modeling and Robust Estimation From a Single-Geometry SAR Amplitude Data Set

This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries’ SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications.

An improved SAR interferogram denoising method based on principal component analysis and the Goldstein filter

ABSTRACTInterferogram filtering is an important data processing step in Interferometric synthetic aperture radar (InSAR) applications, which has a direct impact on the accuracy of the phase unwrapping and digital elevation model (DEM) or deformation results retrieval. An improved synthetic aperture radar (SAR) interferogram denoising method based on principal component analysis and the Goldstein filter is proposed, which can improve the coherence of interferogram remarkably and get more coherent targets. First, homogeneous pixels are identified with stacks of SAR amplitude data, which can obtain the unbiased coherence estimation. Then, the noise phase of one resolution unit is suppressed based on the principal component analysis of multi-baseline InSAR coherence stacks by considering the relationship between pixel size and scattering mechanism. Finally, the remaining noise is smoothed with the iterative Goldstein filter over spatial domain. The proposed method is tested over one deformed and low-coherence region to verify the better performance in the terms of noise reduction and coherence increase.

SAR amplitude probability density function estimation based on a generalized Gaussian model

In the context of remotely sensed data analysis, an important problem is the development of accurate models for the statistics of the pixel intensities. Focusing on synthetic aperture radar (SAR) data, this modeling process turns out to be a crucial task, for instance, for classification or for denoising purposes. In this paper, an innovative parametric estimation methodology for SAR amplitude data is proposed that adopts a generalized Gaussian (GG) model for the complex SAR backscattered signal. A closed-form expression for the corresponding amplitude probability density function (PDF) is derived and a specific parameter estimation algorithm is developed in order to deal with the proposed model. Specifically, the recently proposed "method-of-log-cumulants" (MoLC) is applied, which stems from the adoption of the Mellin transform (instead of the usual Fourier transform) in the computation of characteristic functions and from the corresponding generalization of the concepts of moment and cumulant. For the developed GG-based amplitude model, the resulting MoLC estimates turn out to be numerically feasible and are also analytically proved to be consistent. The proposed parametric approach was validated by using several real ERS-1, XSAR, E-SAR, and NASA/JPL airborne SAR images, and the experimental results prove that the method models the amplitude PDF better than several previously proposed parametric models for backscattering phenomena.

Dictionary-based stochastic expectation-maximization for SAR amplitude probability density function estimation

In remotely sensed data analysis, a crucial problem is represented by the need to develop accurate models for the statistics of the pixel intensities. This paper deals with the problem of probability density function (pdf) estimation in the context of synthetic aperture radar (SAR) amplitude data analysis. Several theoretical and heuristic models for the pdfs of SAR data have been proposed in the literature, which have been proved to be effective for different land-cover typologies, thus making the choice of a single optimal parametric pdf a hard task, especially when dealing with heterogeneous SAR data. In this paper, an innovative estimation algorithm is described, which faces such a problem by adopting a finite mixture model for the amplitude pdf, with mixture components belonging to a given dictionary of SAR-specific pdfs. The proposed method automatically integrates the procedures of selection of the optimal model for each component, of parameter estimation, and of optimization of the number of components by combining the stochastic expectation-maximization iterative methodology with the recently developed method-of-log-cumulants for parametric pdf estimation in the case of nonnegative random variables. Experimental results on several real SAR images are reported, showing that the proposed method accurately models the statistics of SAR amplitude data.

Problems with the retrieval of glacier net surface balance from SAR imagery

AbstractThere are relatively few comparisons between synthetic aperture radar (SAR) observations and glacier mass-balance measurements. More typically, SAR has been deployed to identify changes in the end-of-summer snowline and other facies boundaries. In this paper, we analyze the geophysical processes affecting SAR amplitude data over two Arctic glacier systems in northern Scandinavia to assess the potential of SAR observations for the retrieval of surface balance parameters. Using a backscatter model and in situ data, we identify the controls on SAR imagery in terms of mass-balance measurement and discuss the glaciological parameters which can reasonably be derived from multi-temporal SAR data. Our results show that amplitude SAR imagery, in the absence of in situ measurements, is not capable of providing meaningful mass-balance data. We show that backscatter from temperate glaciers is affected primarily by snow grain-size and density, and therefore processes such as firnification or depth-hoar formation can complicate the analysis of imagery. We conclude that SAR imagery over temperate glaciers can provide valuable proxy information but not direct mass-balance terms.

Fuzzy-Coded Space-Frequency Quantization for SAR Data Compression

In this letter, we propose a new image coding technique, which is a combination of space frequency quantization and a context-based modeling using fuzzy logic. The compression results showed that the proposed coder outperforms the state-of-the-art coders in the rate-distortion sense for compression of processed synthetic aperture radar amplitude data.

Water Storage of the Central Amazon Floodplain Measured with GIS and Remote Sensing Imagery

Interferometric processing of a Space Shuttle–based swath of synthetic aperture radar (SAR) data collected over the central Amazon floodplain reveals one-day decreases in water levels ranging from 1 to 11 cm in mid-October 1994. The decreases, plotted in a geographic information system (GIS), do not appear to be related to water-body size. Instead, the drops correlate with distance between measured location and main river channel. Because the flow routes connecting observed water bodies to a river channel are convoluted, stream network extraction algorithms operating within a GIS are ideally suited to identify flow-path distances. High-resolution digital elevation models (DEMs) are not available for the Amazon Basin; instead, extraction algorithms are applied to a basin-wide mosaic of raster-based SAR amplitude data. SAR amplitudes mimic topography with weak radar returns over open water lying in low elevations and strong returns over non-flooded, higher elevations. As flow-path distance increases, there is a general decrease in the floodplain water level drops. This functional relationship is used to extrapolate the swath of interferometrically based observations beyond their ∼15-percent coverage of the central Amazon floodplain. If the extrapolation is correct, the decreases in water levels sum to a storage change of 4,600 m3/s from an area of 11,800 km2 across the floodplain during midrecessional flow. This value is about 30 percent less than the storage change estimates previously derived from flood routing. The difference likely results from the model-based assumption that water-level fluctuations across the floodplain are equivalent to those in the main river channel. The interferometric observations suggest that this equivalence does not appear to be correct for mid-October 1994.