With the increase of high spatial and temporal resolution SAR data availability, novel applications and information extraction techniques become possible. Among these, the extraction of micro-motion information has the potential to unlock a range of applications, such as infrastructure monitoring, maritime surveillance and natural disaster damage assessment. However, sensors, acquisition modes and products have not been designed with in mind the optimization of micro-motion extraction and its applications, therefore, careful considerations need to take place when selecting the most suitable data and designing processing algorithms. In this paper practical and processing considerations when dealing micro-motion extraction from high-resolution SAR sensors are discussed and supported with experimental results obtained from Capella, Umbra and TerraSAR-X data.

This work consolidates the existing results in the field of information extraction from spaceborne SAR imagery based on Inverse Synthetic Aperture Radar (ISAR) techniques, as well as enhances the understanding of the phenomenology, the models, the processing algorithms, the applications, and the overall value in security applications. The focus is on ISAR based advanced processing methods to explore the potentialities of very high resolution (VHR) SAR data in a range of security related application domains, including maritime, inland water, and land scenarios.

The discrimination between false and real targets is an important challenge in monostatic SAR imaging. Specifically, a deceptive jammer has the capability to introduce false targets in the focused image, potentially hiding real targets and corrupting the scene. One possible solution to mitigate and probably overcome this issue is through the use of multistatic SAR sensors. Analysing images generated from different bistatic pairs, results to the false target appearing in a different position, due to the change in the receivers’ position, while the real targets will remain the same. The purpose of this paper is to analyse the capabilities of different multistatic geometries to reject false targets from the imaging area.

Optimizing images as output of a neural network has been shown to introduce a powerful prior for image inverse tasks, capable of producing solutions of reasonable quality in a fully internal learning context, where no external datasets are involved. Two potential technical approaches involve fitting a coordinate-based Multilayer Perceptron (MLP), or a Convolutional Neural Network to produce the result image as output. The aim of this work is to evaluate the two counterparts, as well as a new framework proposed here, named Neural Knitwork, which maps pixel coordinates to local texture patches rather than singular pixel values. The utility of the proposed technique is demonstrated on the tasks of image inpainting, super-resolution, and denoising. It is shown that the Neural Knitwork can outperform the standard coordinate-based MLP baseline for the tasks of inpainting and denoising, and perform comparably for the super-resolution task.

As space debris poses substantial risks to space-based assets, the need for efficient, high-resolution monitoring and prediction methods is pressing. This paper presents the findings from the project NEU4SST, exploring Neuromorphic Engineering, specifically event-based visual sensing coupled with Spiking Neural Networks (SNNs), as a solution for enhanced Space Domain Awareness (SDA). Our research concentrates on event-based visual sensors and SNNs, offering low power consumption and precise high-resolution data capture and processing. These technologies bolster the ability to detect and track objects in space, addressing key challenges in the Space domain. Our method exceeded previous models by 15% on the informedness metric, demonstrating its potential in improving SDA, and aiding safer, more efficient space operations. Continued research and development in this area are crucial for realising the full potential of Neuromorphic engineering for future space missions.

One of the problems associated with electromagnetic imaging is that the interaction of photons with targets occurs only on part of their surface, namely those exposed to the transmitted energy rays. Imaging of deep localized objects is very hard especially in the presence of short electromgnetic wavelengths. In this paper we propose a new method for through wall imaging, based on photons and sound waves analysis. The technique investigates Doppler analysis in terms of estimating vibrations generated on infrastructures. The proposed method estimates target's vibration energy in order to perform tomographic imaging of man-made objects, such as buildings. Unlike traditional imaging, this technique allows for through wall imaging. The experimental results are distributed over one case study, where we show the to-mographic imaging of a reinforced concrete infrastructure. We consider this preliminary work very promising for future applications performed from the processing of satellite synthetic aperture radar images.

This paper tests a SAR image coregistration method, developed to account for a joint rotation and range/azimuth shift effect in absence of zooming, on foliage penetrating (FOPEN) data. In particular, the method is referred as a constrained Least Squares (CLS) optimization method and, in its basic form, it sharply extracts all patches composing the entire image. Differently, in next developments it applies a detection stage to identify extended areas in the images where patches are then selected. Moreover, it also performs a refinement of the equations in the CLS problem through an iterative cancellation procedure. The performance of this enhanced version of the CLS are made on the challenging Carabas-II VHF-band FOPEN SAR data to demonstrate its effectiveness also in high-resolution SAR images.

This letter extends the constrained least-squares (CLS) optimization method developed to coregister multitemporal synthetic aperture radar (SAR) images affected by a joint rotation effect and range/azimuth shifts enforcing the absence of zooming effects. To take advantage of the structural information extracted from the scene, the method starts with a detection stage that identifies extended targets/areas in the images. The selected tie-points allow the CLS problem to be reformulated to find its (initial) solution based on a robust subset of image blocks. Then, the mean square error (MSE) of each equation evaluated from the initial solution allows to implement an iterative cancellation procedure to further skim the CLS equation set. The effectiveness of the proposed procedure is validated on real SAR data in comparison with the standard CLS.

This study aims at estimating the Earth deformations due to the nuclear test carried out by North Korea on the 3rdof September 2017 by processing a time series of synthetic aperture radar images acquired by the COSMO-SkyMed satellite constellation. For active satellite sensors working in the X-band, phase information can be unreliable if scenarios with dense vegetation are observed. This uncertainty makes difficult to correctly estimate both the interferometric fringes and the information phase delay generated by the variation in the space-time domain of the atmospheric parameters. To this end, in our research we apply the Sub-Pixel Offset Tracking technique, so that the displacement information is extrapolated during the coregistration process. The results reveal an accurate estimate of the spatial displacement of similar pixels due to the nuclear explosion. The work also reveals a hypothetical underground tunnel network.

This letter proposes a coregistration algorithm to compensate for the possible inaccuracy of trajectory sensor during the synthetic aperture radar (SAR) image acquisition process. Such a misalignment can be modeled as a pure displacement in range and azimuth directions and a rotation effect due to different angles of sight. The approach is formalized as a constrained least squares (CLS) optimization problem enforcing a constraint of the absence of a zooming effect between the two SAR images. Moreover, the system equations can optionally be weighted according to local properties between the extracted patches within the quoted couple. Interestingly, the solution can be obtained in closed form, therefore, with a low computational cost. The results of the tests conducted on the 9.6-GHz Gotcha SAR data demonstrate the capability of the strategy to properly register the imagery.

In this letter, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in [1] and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reduced-size vectors, which feed dominant eigenvalues classifier based upon the model order selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on the measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization can be representative of the type of structure which gives raise to the dominant backscattering mechanism.

In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies.

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters' variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system.

In the last two decades, the use of synthetic aperture radar (SAR) for remote sensing purposes has significantly developed due to improvements in the quality and the availability of the images. Two powerful SAR techniques, namely, polarimetry and interferometry, have further increased the range of applications of the sensed data. Using polarimetry, geometrical properties and geophysical parameters, such as shape, roughness, texture, and moisture content, can be retrieved with considerable accuracy, while interferometric information may be used to extract vertical information with accuracy less than 1 cm. In this paper, the potential of using joint polarimetry and interferometry techniques in SAR data (PolInSAR) for the purpose of SAR image classification is investigated. To achieve this goal, we extend a covariance symmetry detection framework to the PolInSAR scenario. The proposed approach will be shown to be able to exploit the peculiar structures of the covariance matrices of PolInSAR images to discriminate structures within the image. Results using real-SAR data are presented to validate the effectiveness of the proposed approach.

This paper deals with detection of oil spills from multipolarization synthetic aperture radar images. The problem is cast in terms of a composite hypothesis test aimed at discriminating between the polarimetric covariance matrix (PCM) equality (absence of oil spills in the tested region) and the situation where the region under test exhibits a PCM with at least an ordered eigenvalue smaller than that of a reference covariance. This last setup reflects the physical condition where the backscattering associated with the oil spills leads to a signal, in some eigendirections, weaker than the one gathered from a reference area where the absence of any oil slicks is a priori known. A multifamily generalized likelihood ratio test approach is pursued to come up with an adaptive detector ensuring the constant false alarm rate property. At the analysis stage, the behavior of the new architecture is investigated in comparison with a benchmark (but nonimplementable) structure and some other suboptimum adaptive detectors available in the open literature. This study, which is conducted in the presence of both simulated and real data, confirms the practical effectiveness of the new approach.

The availability of multiple images of the same scene acquired with the same radar but with different polarizations, both in transmission and reception, has the potential to enhance the classification, detection, and/or recognition capabilities of a remote sensing system. A way to take advantage of the full-polarimetric data is to extract, for each pixel of the considered scene, the polarimetric covariance matrix, the coherence matrix, and the Muller matrix and to exploit them in order to achieve a specific objective. A framework for detecting covariance symmetries within polarimetric synthetic aperture radar (SAR) images is here proposed. The considered algorithm is based on the exploitation of special structures assumed by the polarimetric coherence matrix under symmetrical properties of the returns associated with the pixels under test. The performance analysis of the technique is evaluated on both simulated and real L-band SAR data, showing a good classification level of the different areas within the image.

This paper considers the problem of coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar change detection starting from the availability of image pairs exhibiting possible power mismatches/miscalibrations. The principle of invariance is used to characterize the class of scale-invariant decision rules which are insensitive to power mismatches and ensure the constant false alarm rate property. A maximal invariant statistic is derived together with the induced maximal invariant in the parameter space which significantly compresses the data/parameter domain. A generalized likelihood ratio test is synthesized both for the cases of two- and three-polarimetric channels. Interestingly, for the two-channel case, it is based on the comparison of the condition number of a data-dependent matrix with a suitable threshold. Some additional invariant decision rules are also proposed. The performance of the considered scale-invariant structures is compared to those from two noninvariant counterparts using both simulated and real radar data. The results highlight the robustness of the proposed method and the performance tradeoff involved.

Coherent multipolarization synthetic aperture radar (SAR) change detection exploiting data collected from N multiple polarimetric channels is addressed in this letter. The problem is formulated as a binary hypothesis testing problem, and a special block-diagonal structure for the polarimetric covariance matrix is forced to design a detector based on the generalized likelihood ratio test (GLRT) criterion. It is shown that the structured decision rule ensures the constant false alarm rate property with respect to the unknown disturbance covariance. Results on both simulated and real high-resolution SAR data show the effectiveness of the considered decision rule and its superiority against the traditional unstructured GLRT in some scenarios of practical interest.

This paper deals with coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar (SAR) change detection assuming the availability of reference and test images collected fromNmultiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem, and the principle of invariance is used to come up with decision rules sharing the constant false alarm rate property. The maximal invariant statistic and the maximal invariant in the parameter space are obtained. Hence, the optimum invariant test is devised proving that a uniformly most powerful invariant detector does not exist. Based on this, the class of suboptimum invariant receivers, which also includes the generalized likelihood ratio test, is considered. At the analysis stage, the performance of some tests, belonging to the aforementioned class, is assessed and compared with the optimum clairvoyant invariant detector. Finally, detection maps on real high-resolution SAR data are computed showing the effectiveness of the considered invariant decision structures.

Synthetic aperture radar (SAR) systems are used to form high-resolution images from radar backscatter signals. The fractional Fourier transform (FrFT), which is a generalised form of the well-known Fourier transform, has opened up the possibility of a new range of potentially promising and useful applications that involve the use and detection of chirp signals that include pattern recognition and SAR imaging. In this study a time variant problem associated with the use of the FrFT for SAR processing is addressed and a new algorithm is presented that resolves this problem. Two new FrFT-based SAR processing algorithms are presented namely the FrRDA and the eFrCSA that are shown to improve the well-established range-Doppler and chirp-scaling algorithms for SAR processing. The performance of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection.

The effectiveness of frequency domain processing algorithms in bistatic synthetic aperture radar (SAR) focusing depends critically on the accuracy of the bistatic slant range function approximation. This letter presents a new Chebyshev slant range function approximation that is shown to increase the accuracy of the analytical approximation of the bistatic point target spectrum. The performance of the new method is compared to the conventional Taylor series approximation approach in the generation of the point target spectrum. The new approach is shown to provide a more accurate approximation of the slant range function with negligible increase in processing requirements compared to the traditional Taylor series approximation. The accuracy improvement is shown to yield a more accurate spectrum that can be exploited in bistatic SAR focusing algorithms.

Bistatic synthetic aperture radar (BSAR) provides strategical, technical and economical advantages in radar imaging. Motions and micro-motions of objects in an illuminated scene introduces Doppler and micro-Doppler effects in the received radar echoes. Combining the advantages introduced by the bistatic configuration and the usefulness of the micro-Doppler signature characterization will provide a powerful tool for military and civil remote sensing applications such as target recognition and classification. In this paper, a vibrating micro-Doppler signature for a BSAR system with fixed receiver is analyzed and compared to the signature obtained in a monostatic SAR system. The micro-Doppler effect is derived for a vibrating target in the bistatic SAR. The corresponding bistatic factor is shown to be a function of the bistatic acquisition geometry. Also, the effect of the target vibration on the focused image is shown to be influenced by the acquisition geometry. The derived model is useful for micro-Doppler classification. Simulations for 94 GHz and 10 GHz are given and the results confirm the derived model.

This paper deals with the problem of Constant False Alarm Rate (CFAR) detection of thermal anomalies in multispectral satellite data. The goal is to provide robustness to the algorithm proposed in [1], with respect to the presence of outliers in the analysis window. In [1], data from 4 μm and 11 μm MODIS bands, that are statistically correlated, are re-projected through a Principal Component Analysis (PCA) to obtain uncorrelated data, a necessary condition for the final stage of CFAR detection. Unfortunately, the sample covari-ance matrix used in the PCA can be strongly affected by the presence of thermal anomalies, therefore a robust estimator is needed. To this aim, the Minimum Covariance Determinant estimator (MCD) is introduced in the PCA yielding an analysis that is little influenced by the presence of anomalies while provides results similar to the usual PCA for uncontaminated data. Experimental results have shown that many detections can be missed if the MCD estimator is not used in the presence of anomalies, even if their number is not so high but their values are able to modify significantly the sample covariance matrix. The robust multiband CFAR algorithm has been applied to a MODIS image and results have been compared with those from NASA-DAAC MOD14.