The proposal of deep learning (DL) solutions for SAR image despeckling is recently widespread. Such solutions have been mainly designed in a DL perspective by leveraging the training and validation stage on the use of typical norm-based cost functions. For going beyond the DL perspective, in this paper we propose a SAR based validation stage by using SAR assessing metrics in the design and hyper-parameter selection of neural networks. In a first phase, SAR assessing metrics may be used only as validation metrics with the aim of highlighting critical issues that can not be spotted with standard image-processing quality metrics. In a second phase, the same SAR assessing metrics may be used directly for enhancing the DL solution by the addressing specific issues arisen during the previous SAR based validation stage. To this aim, three different DL SAR despeckling solutions and four different SAR assessing metrics have been considered. The outcome of this analysis shows the importance of including SAR knowledge in the training and validation stages of the design of a DL solution for SAR image despeckling.

Breast cancer represents one of the most impactful diseases for both human health and healthcare system. The use of cutting-edge and innovative technology solutions is paramount to allow a proper and early diagnosis, which also would mitigate its impact on the society. In this framework, the proposed work explore the use of safe, non-ioninzing microwave radiation with dense deep learning models for the detection and localisation of breast tumor, providing spatial information to support medical decision and personalised medicine. After a screening step for the identification of malignant breasts, a tumor spatial probability map is estimated by means of fully -connected deep learning models. To this aim, an in-house, two-dimensional MRI-derived breast database consisting of 160,000 profiles was adopted, and the localisation performance was quantified by adopting proper quality metrics. The obtained results confirm the potentialities that deep learning and microwave technology can have as emerging solutions in the healthcare system for improving the quality of patients' life.

Synthetic Aperture Radar (SAR) serves as a fundamental system for Earth observation. Specifically, Polarimetric SAR (PolSAR) sensors capture images of diverse polarization scenes, enhancing the retrievable information. Due to their coherent nature, SAR images represent complex data affected by multiplicative noise known as speckle. The presence of this noise impedes image interpretation, making speckle removal a crucial pre-processing step for further applications. Recently, various Deep Learning (DL)-based methods have been proposed for speckle removal in PolSAR data, relying on different strategies for constructing training datasets. This complicates practical comparisons due to the absence of ground truth. In this paper, a comparison of the impact of different PolSAR despeckling methods on classification applications is studied. Some different PolSAR despeckling filters shows the different ability on improving the classification. In general, DL-based methods have greater improvement ability and potential.

The status of coastal zones has a great impact on economy and population and, therefore, the monitoring of shoreline is a crucial task. In order to have a wide scale and rapid monitoring, remote sensing represents a perfect opportunity, In particular, the ability of working all day in any meteorological conditions makes Synthetic aperture radar (SAR) system attractive for such task. At the same time, the ability of rapidly processing huge data makes deep learning an appealing solution. The aim of this work is to examine the effectiveness and potential of utilizing a deep learning solution for identifying and extracting coastlines from satellite SAR images. Firstly, a specific training dataset has been created using SAR data and ancillary information for retrieving position of coastline. Finally, the shoreline extraction has been performed as deep learning based segmentation task.

SAR despeckling is a fundamental task for improving the interpretation of SAR images and the performance of further tasks (classification and detection). The spreading of deep learning solutions has highlighted one main common issue: the availability of a training dataset. The construction of a training dataset is, indeed, limited by the absence of real ground truth. In this paper, an analysis of different approaches for constructing training dataset based on either fully simulation or real data has been carried out. The results show the importance of including real data properties within the training dataset.

Synthetic Aperture Radar (SAR) are fundamental tools for target classification and detection in the different applicative scenarios (military, agriculture, etc…). Extracting geometrical features of a target strongly help in its detection and classification. Indeed, the extraction of Attribute Scattering Center (ASC) characteristics is widely used from improving SAR target recognition. ASC extraction is a challenging task that requires the accurate estimation of tiny details (shape, orientation, etc…) from the SAR target backscattering. In this work, the aim is to exploit the potential of deep learning for ASC extraction. Considering a simulated environment, a deep-learning based classification solution is defined for extracting the target characteristics.We proposed a multi classification heads VGG solution, which can extract scattering parameters from complex images and also guarantee the estimation accuracy.

The building height estimation of urban environments is a challenging problem for Synthetic Aperture Radar (SAR). SAR Tomography (TomoSAR) conducts a series of acquisitions to realize a 3D reconstruction. Classical 3D focusing algorithms’ performance tends to be affected by the limited number of acquisitions, and the uneven baselines. Inspired by the advanced performance of TSNN on forest height estimation, in this study, we apply TSNN to reconstruct building height and we compare the obtained results with a classical Tomography approach. The experimental results are based on the data acquired by the DLR’s ESAR sensor at L-band over Dresden, Germany. The results illustrate the possibility of using the deep learning-based approach for building height estimation on urban environments.

Forest measurement is crucial for tracking climate change and quantifying the global carbon cycle. Synthetic Aperture Radar Tomography has become an effective technique to realize 3D forest structure monitoring. Recently a Deep Learning approach, named TomoSAR Neural Network (TSNN), has been proven a valid method for forest height and underlying topography estimation with polarimetric TomoSAR data. In this study, we evaluate the generalization ability of TSNN in working in different target areas, with different sensors and acquisition parameters. The experimental results demonstrate the robustness and generalization ability of TSNN.

Polarimetric Synthetic Aperture Radar (PolSAR) data plays an important role in Earth observation. In this field, deep learning (DL) method can achieve high classification performance on PolSAR image dataset and, in particular, vision transformer(ViT) has achieved significant breakthroughs. Compared with convolutional layers, ViT is able to extract global feature and find the global relationship, which can help to improve the performance of classification. The aim of this work is to exploit the potential of ViT for PolSAR classification. In this case, we propose a simple classification method based on transformer, called Pol-Trans. The PolSAR data is pre-processed to get the coherency matrix. Then the image patch of the pixel to be classified is flattened as the tokens. Finally, with the class embedding, our transformer can output the classification result of the PolSAR data. Our experiments on the ALOS2 PolSAR dataset of San Francisco shows the effectiveness of our method.

Synthetic aperture radar (SAR) are fundamental system for Earth Observation. In particular, polarimetric SAR (PolSAR) sensors provide images of a scene at different polarizations, enriching the information that can be retrieved. Due to their coherent nature, SAR images are complex data affected by a multiplicative noise, called speckle. The presence of this noise hinders the interpretation of images, making speckle removal a fundamental preprocessing step for further applications. Several deep learning (DL)-based approaches have been recently proposed for speckle removal in PolSAR data relying, due to the lack of a real ground truth, on different strategies for constructing training datasets making a real comparison complicated. In this work, a study on the construction of a training dataset for PolSAR despeckling is proposed. In particular, considering the analysis recently conducted on the construction of the dataset for training supervised neural networks for SAR amplitude despeckling, the aim is to extend such studies to the PolSAR case. In particular, the commonly used multitemporal approach, relying on the stack of real data, is compared with the so-called hybrid approach, in which a mixture of real and synthetic data is proposed. A specific DL solution has been chosen for such comparison, but the analysis could be extended to whatever supervised neural network. Moreover, for the sake of completeness, results are also compared with a well-assessed PolSAR despeckling filter in the literature.

The accurate quantitative estimation of the electromagnetic properties of tissues can serve important diagnostic and therapeutic medical purposes. Quantitative microwave tomography is an imaging modality that can provide maps of the in-vivo electromagnetic properties of the imaged tissues, i.e. both the permittivity and the electric conductivity. A multi-step microwave tomography approach is proposed for the accurate retrieval of such spatial maps of biological tissues. The underlying idea behind the new imaging approach is to progressively add details to the maps in a step-wise fashion starting from single-frequency qualitative reconstructions. Multi-frequency microwave data is utilized strategically in the final stage. The approach results in improved accuracy of the reconstructions compared to inversion of the data in a single step. As a case study, the proposed workflow was tested on an experimental microwave data set collected for the imaging of the human forearm. The human forearm is a good test case as it contains several soft tissues as well as bone, exhibiting a wide range of values for the electrical properties.

SAR (Synthetic Aperture Radar) sensors are fundamental tools for the Earth Observation. Actually, SAR images are affected by a multiplicative noise speckle that require a filtering step crucial for further application: classification, detection, etc. As in all image processing task, deep learning has been widely used for SAR image despeckling in the last years. Many methods have been proposed with different architectures, cost functions, training approaches, showing impressive performance. Actually, differently from natural domain denoising, an extensive comparison of such methods is still missing. As matter fact, such methods focus their comparison on few testing images. The aim of this paper is to propose and carry out the comparison among DL (Deep Learning) based methods not only in the testing phase but explointg the validation dataset used during the training for evaluating performance on SAR based metrics. The aim is to give an assessment on a more wide scenarios.

Synthetic Aperture Radar (SAR) are fundamental systems for the Earth Observation, providing images in any meteorological condition, during day and night. Due to their coherent nature, SAR images are complex data affected by a multiplicative noise called speckle impairing their interpretation. Therefore, speckle removal is a fundamental task for further applications. Following the interesting results obtained for single-channel despeckling, a deep learning approach is proposed for Polarimetric SAR (PolSAR) despeckling. In particular, the aim is to extend the outcome obtained on the construction of the dataset for SAR amplitude despeckling to the PolSAR case. In order to take fully advantage of such approach a multi-objective neural network has been considered. In particular, the hybrid approach has been used for creating a dataset for training a network following supervised approach. Comparison with state of art methods on real PolSAR images have shown the good versatility of such approach: the hybrid approach together with the multi-objective cost function lead the network to a good trade-off between noise suppression and texture preservation.

Forests play an important role in the global carbon cycle, and subsequently global climate change. Synthetic Aperture Radar Tomography (TomoSAR) can achieve three-dimensional forest structures relying on the multibaseline image acquisition. At present, plenty of TomoSAR approaches are based on fully polarimetric TomoSAR datasets which require costly data acquisition. The aim of this paper is to exploit the potential of deep learning for retrieving forest height by using single polarimetric data, going beyond the limitation of the requirement for full polarization. We design a fully connected network handling the forest height reconstruction problem from a classification task perspective. The network is trained using the covariance matrix elements of single polarimetric images acquired by ONERA over Paracou region as input, while LiDAR data acts as reference. Experimental results generally show good performance for forest height and underlying topography reconstruction and, a good robustness if compared with the results driven by fully polarimetric images.

Forest characterization and monitoring are highly important for tracking climate change, utilizing ecology resources, and biodiversity applications. Synthetic Aperture Radar Tomography (TomoSAR) provides the opportunity to reconstruct three-dimensional structures of the penetrable media relying on multi-baseline image acquisition. In forest applications, TomoSAR serves as a powerful technical tool for reconstructing forest height and underlying topography. Presently, a number of reconstruction methods are based on fully polarimetric TomoSAR datasets which require costly data acquisition. The aim of this paper is to go beyond the limitation of the requirement for full polarization by extending Tomographic SAR Neural Network (TSNN), a neural network for TomoSAR, to the case of single-polarimetric (SP) and dual-polarimetric (DP) TomoSAR data for retrieving forest height and underlying topography. Experimental results indicate that TSNN trained by SP or DP TomoSAR data is a powerful candidate to estimate forest height and underlying topography with high accuracy.

Synthetic Aperture Radar (SAR) images are impaired by the presence of speckle. Despite the deep interest of scholars in the last decades, SAR image despeckling is still an open issue. Among different approaches, recently, many Deep Learning (DL) methods have been proposed following both supervised and unsupervised training approaches. There are two main challenges within the supervised framework: training data, and cost functions. Our approach builds training datasets which are varied and realistic using a multi-category Generalized Gaussian Coherent SAR simulator. It allows modeling a variety of SAR scenarios beyond the fully developed speckle hypothesis, which is only valid in homogeneous areas. Such multi-category simulated speckle is then applied to a noise-free reference obtained by multi-looking a temporal stack of actual SAR images in order to obtain the noisy input. We design an effective multi-objective cost function that accounts for texture, edge, and statistical properties preservation. We show the superiority of our approach assessing numerically and quantitatively its performance with three different SAR datasets.

Forest height and underlying terrain reconstruction is one of the main aims in dealing with forested areas. Theoretically, Synthetic Aperture Radar Tomography (TomoSAR) offers the possibility to solve the layover problem, making it possible to estimate the elevation of scatters located in the same resolution cell. This paper describes a deep learning approach, named Tomographic SAR Neural Network (TSNN), that aims at reconstructing forest and ground height using multipolarimetric multibaseline (MPMB) SAR data and Light Detection and Ranging (LiDAR) based data. The reconstruction of the forest and ground height is formulated as a classification problem, in which TSNN, a feed-forward network, is trained using covariance matrix elements as input vectors and quantized LiDAR-based data as the reference. In our work, TSNN is trained and tested with P-band MPMB data acquired by ONERA over Paracou region of French Guiana in the frame of the European Space Agency’s campaign TROPISAR and LiDAR-based data provided by the French Agricultural Research Center. The novelty of the proposed TSNN is related to its ability to estimate height with a high agreement with LiDAR-based measurement and actual height with no requirement for phase calibration. Experimental results of different covariance window sizes are included to demonstrate TSNN conducts height measurement with high spatial resolution and vertical accuracy outperforming the other two TomoSAR methods. Moreover, the conducted experiments on the effects of phase errors in different ranges show that TSNN has a good tolerance for small errors and is still able to precisely reconstruct forest heights.

Biometrics showed their usefulness in several applications, systems with fingerprint or face identification are well accepted in several fields with possibly different constraints like, for example, forensic or smartphone unlocking. In this paper, a novel ultrasound prototype for person identification and authentication has been designed, built and tested. The system, which acts as a sonar, measures the pressure wave backscattered from a moving hand. Such signal is subsequently processed by means of time/frequency analysis and a deep learning detector is implemented in order to identify/authenticate the user based on some peculiarity in the gestures execution. The proposed solution is cheap and, due to the high adaptability, allows different security levels. Promising results are obtained after tests carried out with the help of 10 volunteers.

Subsurface non-destructive exploration is of interest for several applications which span from archeology and civil engineering to safety and security. Among the available remote sensing imaging modalities, ground penetrating radar (GPR) seems very attractive for the detection and characterisation of buried targets. This technology exploits electromagnetic signals in the microwave band to perform the exploration in a non-invasive way. Unfortunately, conventional GPR images require an expert user to be interpreted and do not provide quantitative information about the buried objets. In this framework, this paper explores the use of artificial neural networks for quantitative multiple-input-multiple-output tomographic ground penetrating radar to improve the reliability of the quantitative tomographic recovery, speeding up the nonlinear inversion and providing a user-friendly image which is easy to be interpreted even for a non-expert user.

A fast and precise detection of floating plastics debris is necessary for monitoring and saving the sea ecosystem. Recent studies have demonstrated how remote sensing (and in particular satellites) can be helpful in such detection. In particular, data provided by satellite sensors allow to continuously monitoring wide areas of our planet interested by plastic litters. In this work, the possibility of exploiting different optical remote sensing satellite methods is investigated: the analysis is conducted starting from multi-spectral data and moving tom hyperspectral one. Data acquired from Sentinel 2 and from PRISMA sensors are considered showing the added value of these systems in the detection of marine litter within marine areas.

InSAR phase are affected by noise that impairs the performance of applications such as topography, 3D reconstruction, DEM profile, etc. Therefore a denoising step is fundamental. In the last decades many methods for InSAR phase denoising have been proposed such as Local, Non Local and other kind of filters. Inspired by the great success of deep learning in image denoising, methods relying on convolutional neural networks have been proposed recently. In this work, inspired by the outcomes of an amplitude despeckling filter, a multi-objective neural network for interferometric phase denoising is proposed: a cost function composed of three terms takes into account fringe, edges and statistical preservation. The encouraging results are validated quantitatively and qualitatively on a simulated dataset.

Elevation estimation of canopy and ground is one of the main aims in dealing with forest scenario using Synthetic Aperture Radar (SAR) Tomography. Theoretically, SAR Tomography (TomoSAR) provides layover solution, allowing to reconstruct the elevation of the different contributions collapsing in the same resolution cell. TomoSAR is commonly applied on both urban and vegetated areas. Within the latter scenario, one of the most interesting outcomes of TomoSAR is the possibility of separating the canopy and ground, allowing the reconstruction of their height maps. Within this paper, we propose a Deep Learning (DL) based method for TomoSAR. In particular, a neural network was trained for predicting the elevation value of canopy and ground of an area under investigation, based on a stack of SAR fully polarimetric multi-baseline acquisitions. The method uses the Light Detection And Ranging (LiDAR) data as reference and exploit a classification approach. The process was operated on a tropical forest over the TropiSAR2009 test site in Paracou, French Guiana. Testing results on real data are presented showing interesting results.

In the framework of deep learning for synthetic aperture radar (SAR) speckle reduction, the methods presented in the literature mainly focus on the definition of new architectures and cost functions for better catching and preserving the properties of a real SAR image. The achieved results are interesting and promising but with many left open issues. The main critical problem, shared by all the methods, is the construction of a training dataset. This is due to the lack of a noise-free reference. In this work, a comparison among different training approaches (synthetic, multitemporal, and hybrid) is carried out in order to analyze their benefits and drawbacks. Four convolutional neural network (CNN)-based methods have been trained with the three different datasets for their assessment. Results on real SAR images have been carried out showing the peculiarities of each training approach.

Among the large number of synthetic aperture radar (SAR) image despeckling approaches existing in literature, nonlocal (NL) filters have received a desirable boost. However, often NL approaches define the similarity criterion based on model assumptions, such as a fully developed speckle model. This assumption may not be verified in high-resolution images of urban environments. To address this issue, a standalone model-free despeckling framework is proposed in this article. The presented approach provides a generic framework for denoising a variety of SAR products, from a single-intensity/amplitude image to polarimetric and interferometric SAR data. In particular, the method is based on the empirical distributional similarity between the patch containing the pixel to be recovered and the patch containing a similar candidate pixel. To decide whether the patches follow a similar distribution, the Kolmogorov–Smirnov test is adapted. Finally, the restoration process aggregates the selected similar pixels based on their relative importance derived from their distribution similarities. To mitigate the blurring effect and preserve the resolution, the inhomogeneity of the ratio image is used to perform the bias reduction step. The designed generic despeckling filter was tested on different products of SAR data. The results show that the method proves to be an unbiased restoration approach and is able to preserve structures and textures. It works fully automatically and efficiently with single and multilook (and multichannel) images.

Interferometric Synthetic Aperture Radar is an effective and widely adopted tool for earth observation. Based on interferograms it is possible to infer several information about the observed area. Two main problems affecting the interferogram can limit its application: phase noise and phase wrapping. In this paper the attention is focused on the first issue. Several algorithms have been developed for interferogram restoration. Given the wide spread of Deep Learning (DL) in the framework of image processing, DL based algorithms have been proposed for interferogram denoising. Most of the efforts have been devoted in designing specific network architectures or training dataset, rather than on the definition of a specific cost function, well suited for the problem under investigation. The aim of this manuscript is to define a new multi-objective cost function, specifically thought for the interferograms restoration problem: the idea is to provide a cost function able to take into account multiple aspects of the data under investigation (i.e. multi-objective). The cost function is implemented within a Convolutional Neural Network and a specific realistic training dataset is built, to account the main characteristics of real interferograms. The final outcome of the paper is the proposal of a new robust and accurate interferometeric phase denoising algorithm (namely InSAR-MONet ), able to remove undesired noise and, at the same time, able to preserve important phase details. The assessment of the method is conducted on simulated and real datasets, comparing quantitatively and qualitatively InSAR-MONet with the state of the art interferometric denoising algorithms.

Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU methods are limited by the Itoh condition, which is from the PU researchers’ experience and has critical challenges under strong phase noises or violent phase changes. Recently, advanced learning-based 2-D PU methods could break through the limitation of the Itoh condition owing to their data-driven frameworks, offering promising results in terms of both the speed and accuracy. The one-step learning-based PU method, as one of the representatives, retrieves the unwrapped phase directly from the wrapped phase through regression. However, the main disadvantage of one-step learning-based PU is that it usually blurs the output unwrapped phase due to its$L_{2}$loss, that is, it cannot guarantee the congruency between the rewrapped interferometric fringes of the PU solution and the input interferogram. To solve this problem, we propose a one-step 2-D PU method based on the conditional generative adversarial network (referred to as PU-GAN), which treats 2-D PU as an image-to-image translation problem. The generator in PU-GAN can be trained to generate the unwrapped phase through minimizing a$L_{1}$-norm loss based on a U-Net architecture, while simultaneously the corresponding discriminator can learn an adversarial loss by a structure of Patch-GAN that tries to classify if the output unwrapped phase image is real or fake. Both a theoretical analysis and the experimental results show that the proposed method outperforms the representative algorithmic model-based and learning-based 2-D PU methods.

In this paper the effectiveness of a CNN based interferometric phase unwrapping algorithm combined with phase noise filtering is analysed. In particular, the considered processing chain relies on a pre-processing step with the nonlocal filter InSAR-BM3D followed by a deep CNN solution for restoring the absolute phase. The analyses is conducted on simulated data with different coherence values and aims at comparing the performance of the unwrapping with and without the pre-processing step. This paper is the first step towards a unique deep learning solution for jointly unwrapping and restoring the absolute phase.

Due to the constraint of minimum antenna area, azimuth ambiguity resolving is a challenging task in the ship focusing for single-channel synthetic aperture radar (SAR) mounted on high-speed maneuvering platforms. In order to accommodate the issues, a ship focusing algorithm based on azimuth ambiguity resolving is proposed in this paper. For ship SAR imaging with small-aperture data, the energies of different targets are separated in Doppler domain with different Doppler ambiguity numbers. Thus, the Doppler ambiguity number of a single target can be estimated by residual envelope inclination. Then, the target can be accurately focused and located at the correct position by the known Doppler ambiguity number. After the operation of each target is completed, the focusing SAR image of the whole scene can be obtained. Finally, simulation results are presented to validate the proposed algorithm.

This paper proposes to apply the local-plane model in urban tomography imaging to increase the detection probability and the regularity of the persistent scatterers (PSs). A local-plane generalized likelihood ratio test (LP-GLRT) algorithm is developed, which shows a better adaption to the nonplanar architectures and terrain when compared with the Multi-look GLRT algorithm. Experiments on Terra-SAR images are presented to validate the algorithm.

This paper deals with the combination of multi-pass images obtained by COSMO-SkyMed SAR satellite over the urban area of Napoli. The coherent processing can improve geometric resolution and the image quality of long-term coherent regions. At last, the coherently and incoherently combined images are fused tighter based on the coherence to increase the readability of the low-coherence regions.

SAR image interpretation is always impaired by speckle that is a multiplicative noise due to interference among the backscatterings from targets inside a resolution cell. Many algorithms for both single and multi-channel SAR despeckling have been proposed in the last forty years following different approaches. Recently, a multi-objective convolutional neural network, named MONet, has been proposed for single channel SAR despeckling. It relies on a mulit-objectvie cost function that takes into account three main aspects of the SAR images: noise removal, details and statistics preservation. Inspired by MONet, in this paper a deep learning method for InSAR phase filtering is proposed. The idea is to benefit from the multi-objective cost function defined in MONet that seems to perfectly fit with the interferogram denoising. This is the first step towards a solution able to provide a complete processed multi-channel product.

Among the different deep learning-based methods proposed for SAR image despeckling, the main issue seems to construct reliable training data sets. In the statistical-based solution MONet, which assumes square root Gamma distributed speckle in the simulation, the authors showed that despeckling results on actual SAR images are stringently related to the considered training dataset and its statistical distributions. This paper develops realistic simulated data sets for feeding the MONet architecture, including backscattering mechanisms arising in different existing SAR scenarios. We consider a generalized Gaussian coherent scatterer model for SAR correlated clutter simulation for this aim. The use of such simulation has a twofold effect within the considered framework: from one side, it allows generating several noisy patches, used as input data; on the other, it allows including different speckle distributions for different actual SAR scenarios. Results on SAR images show the effectiveness of such simulation.

Since path curvature becomes severer for higher orbit synthetic aperture radar (SAR), the stripmap mode may not provide a reliable azimuth resolution under different look angles or at different positions. Beam steering is especially valuable herein for adjusting the azimuth resolution under different observation conditions by designing the antenna steering rate. Moreover, considering that the large range migration and center range variation in the squint mode may increase the echo length and reduce the achievable scene width, we proposed a novel 2-D beam steering (TDBS) method, which promises not only a required azimuth resolution but also a wide swath (or shortened echo length) at squint when cooperated with the variable interpulse time (VIPT) technique. The simulation results obtained under different look directions are shown to validate the effectiveness of the proposed beam controlling method.

Deep learning (DL) in remote sensing has nowadays become an effective operative tool: it is largely used in applications, such as change detection, image restoration, segmentation, detection, and classification. With reference to the synthetic aperture radar (SAR) domain, the application of DL techniques is not straightforward due to the nontrivial interpretation of SAR images, especially caused by the presence of speckle. Several DL solutions for SAR despeckling have been proposed in the last few years. Most of these solutions focus on the definition of different network architectures with similar cost functions, not involving SAR image properties. In this article, a convolutional neural network (CNN) with a multi-objective cost function taking care of spatial and statistical properties of the SAR image is proposed. This is achieved by the definition of a peculiar loss function obtained by the weighted combination of three different terms. Each of these terms is dedicated mainly to one of the following SAR image characteristics: spatial details, speckle statistical properties, and strong scatterers identification. Their combination allows balancing these effects. Moreover, a specifically designed architecture is proposed to effectively extract distinctive features within the considered framework. Experiments on simulated and real SAR images show the accuracy of the proposed method compared with the state-of-art despeckling algorithms, both from a quantitative and qualitative point of view. The importance of considering such SAR properties in the cost function is crucial for correct noise rejection and details preservation in different underlined scenarios, such as homogeneous, heterogeneous, and extremely heterogeneous.

The curved trajectory and long synthetic aperture time of medium-earth-orbit (MEO) synthetic aperture radar (SAR) lead to a two-dimensional spatial variation in the signals. Traditional methods treat the range and azimuth variations separately, and usually suffer from high computational complexities. We investigate the Doppler rate distribution across a large scene, and exploit an optimal imaging coordinate system, in which the MEO SAR signals satisfy the azimuth-shift-invariant property. The additional processing of the azimuth spatial variation in MEO SAR imaging algorithms can be avoided, and the efficiency of the image formation processor can be improved. Finally, processing of simulated stripmap-mode data with 2-m resolution can validate the proposed algorithm.

SAR images are affected by multiplicative noise that impairs their interpretations. In the last decades several methods for SAR denoising have been proposed and in the last years great attention has moved towards deep learning based solutions. Based on our last proposed convolutional neural network for SAR despeckling, here we exploit the effect of the complexity of the network. More precisely, once a dataset has been fixed, we carry out an analysis of the network performance with respect to the number of layers and numbers of features the network is composed of. Evaluation on simulated and real data are carried out. The results show that deeper networks better generalize on both simulated and real images.

ISAR 2D imaging is obtained by projecting the 3D structure target onto a 2D imaging plane. The angle between the imaging plane and the target spinning axis has a great influence on the projection result. Generally, this angle is neglected, which results in the target imaging has smaller size than the real target. This is not conducive to the further application of target detection and target recognition. In a short observation time, this angle cannot be estimated by monostatic radar. In order to solve such a problem, this letter proposes a method using bistatic radar to estimate the angle and accomplish accurate calibration. First, bistatic ISAR model and bistatic echo signal of spinning target are modeled. Then combining monostatic and bistatic 2D imaging, the angle can be calculated based on several prominent scatterers. Recalibration is performed based on this angle. Finally, the effectiveness of the proposed method is verified by different simulation experiments.

Backscattering separation coming from ground and canopy is one of the main aims in dealing with forest scenario using synthetic aperture radar (SAR) tomography. Theoretically SAR tomography (TomoSAR) provides layover solution, but in practice, insufficient vertical resolution using typical reconstruction approaches may not be sufficient for identification of the vertically aligned scatterers. To cope with this intrinsic issue, we proposed a method that separates the ground and canopy backscatterings based on Random- Volume-over-Ground (RVOG) model and by employing the generalized likelihood ratio test (GLRT) detection schemes over the covariance matrix. Such a separation allows identification of interference of the backscattering, which simply brings the possibility to resolve and separate ground and canopy superposition in the tomogram. Experimental validation of the proposed methodology is provided using a real data set acquired by the ONERA SETHI in the framework of the ESA's campaign, TropiSAR.

This paper deals with the characterization of permanent scatterers in polarimetric synthetic aperture radar (SAR) images of urban environment. To this aim, the main purpose of this paper is to investigate how spaceborne SAR tomography (TomoSAR) can be employed to identify and distinguish the target scattering mechanisms. Along this, the conventional H-α classifier can be adapted to the reconstructed polarimetric coherence matrix, i.e. T, in a multi-dimensional space. However, dealing with multitemporal multi-baseline satellite images, the accurate tomographic reconstruction requires permanent scatterers between all the acquisitions. To cope with this issue, a generalized likelihood ratio test (GLRT)-based tomographic approach for polarimetric SAR tomography is developed. The proposed framework of scatterer detection and characterization is evaluated using TerraSAR-X polarimetric multi-baseline data sets over an urban area in France.

In this paper, an experimental assessment of the linear sampling method (LSM) on the Georgia Institute of Technology multi-frequency multistatic dataset is proposed. The employed approach belongs to the class of linear, qualitative approaches, which seems to be quite promising due to the low computational complexity. Such a method only involves a singular value decomposition (SVD) of the scattering matrix, and this feature, together with its ease of implementation, make it a good candidate for the use in several practical applications.

Earth observation using spaceborne sensors with short revisiting time has forgone the limits of conventional assessment methods. In this paper, we investigate the use of SAR Tomography (TomoSAR) to monitor infrastructures, using Morandi Bridge in Genoa, Italy, as a case study. Morandi Bridge suffered a partial collapse due to a structural failure on August 14, 2018. The main issues to be considered for using TomoSAR as a means of an early warning system in structural health monitoring are discussed, highlighting the differences respect to monitoring applications dealing with subsidence and deformation of extended areas. A set of 70 Sentinel-1A images of the bridge over a period of three years prior to the bridge collapse are analyzed using 5D TomoSAR based on Sup-GLRT scatterers detection technique. Preliminary results indicate that possible temporal and thermal deformations of the static structure can be estimated using the method considered.

In SAR domain many application like classification, detection and segmentation are impaired by speckle. Hence, despeckling of SAR images is the key for scene understanding. Usually despeckling filters face the trade-off of speckle suppression and information preservation. In the last years deep learning solutions for speckle reduction have been proposed. One the biggest issue for these methods is how to train a network given the lack of a reference. In this work we proposed a convolutional neural network based solution trained on simulated data. We propose the use of a cost function taking into account both spatial and statistical properties. The aim is two fold: overcome the trade-off between speckle suppression and details suppression; find a suitable cost function for despeckling in unsupervised learning. The algorithm is validated on both real and simulated data, showing interesting performances.

Although the first filtering algorithms have been proposed more than 30 years ago, despeckling of synthetic aperture radar images is still an open issue. A new boost has been provided by nonlocal (NL) means filters. The idea of NL filters is to move from the exploitation of spatial neighboring pixels to the exploitation of similar pixels found across the image. The difference between the NL algorithms is mainly related to the definition of the similarity between pixels and how similar pixels are exploited in the restoration process. Generally, to define the similarity, the patches are adopted. In this paper, a new similarity criterion for selecting similar pixels is presented. It is based on the definition of the ratio patch between the patch containing the pixel to be restored and the patch containing a candidate similar pixel. If the two pixels are similar, it is expected that the corresponding ratio patch will follow a specific statistical distribution. A modified version of the Kolmogorov-Smirnov distance is introduced to decide whether the statistical distribution of the ratio patch follows the expected one. To reduce the possible artifacts, anisotropy is exploited. Considering the proposed approach, the designed algorithm turns to be unbiased, able to provide the restored solution without any thresholding procedure, in which the tuning is substantially unsupervised and able to work with both single-look and multilook images. The algorithm has been tested on different simulated and real data. Qualitative and quantitative analyses validate the proposed approach, showing very good despeckling capabilities.

This paper explores the main issue surrounding the multidimensional synthetic aperture radar (SAR) image focusing techniques, suchlike those caused by the atmosphere propagation delays or by residual platform motion. The problem brings unknown contributions to the phases of complex received signal that it is generally independent of acquisitions track to track and leads spreading and defocusing in multi-dimensional space. To deal with these issues, in this paper an auto-focusing procedure based on sharpness optimization of the reconstructed signal has been employed. The main concern about this technique is that sharpness optimization by itself however can introduce unwanted and uncontrollable vertical shifts in the focused image. To tackle this issue, the phase error is estimated by multiple integration of second derivative of the phase with respect to baseline. The estimation of the calibration phase is performed by optimizing contrast or entropy of the vertical profile with the constraint of a zero phase derivative. In this way, unwanted vertical shifts are avoided and the correct height reference is preserved. Experimental results from the proposed method are evaluated by vertical profile reconstruction performance in the controlled conditions by simulated dataset over the forested area and multi-baseline data acquired by ONERA in Guyana in the frame of European space agency's campaign TROPISAR.

Synthetic aperture radar (SAR) tomography is the most typical approach to generate the elevation map of the observed scene through the 3D imaging from multi-baseline acquisition. Typically, the nominal scatterers can be derived by evaluation of the presented peaks in the reconstructed scattering reflectivity through the array signal processing methods. In this paper, we investigate the possibility to improve the height reconstruction process and achieve a reliable elevation map by jointly estimating and regularizing the solution of array processing techniques. In particular, an a priori is added to the cost function of desired parameter estimation in the processing chain. The algorithm is evaluated using simulated data correspond to the E-SAR airborne sensor of the German Aerospace Center (DLR).

This paper addresses the problem of SAR Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers in presence of partially correlated Gaussian clutter. TomoSAR is a multidimensional imaging technique that has proven its ability in localizing the scatterers, reconstructing the elevation profile of the structures on the ground (3D reconstruction) and estimating the temporal deformations and thermal dilations of the scene (5D reconstruction). In the literature statistical based TomoSAR reconstruction refers to a signal model where in each range-azimuth resolution cell one or more scatterers are interfering in presence of noise and clutter signals, modeled as zero-mean complex circular white Gaussian random vectors. In this paper, we propose to extend a generalized likelihood ratio test (GLRT) detector, proposed by the authors and denoted Fast-Sup-GLRT, to a different signal model, where a correlated clutter model is considered. Results on TerraSAR-X real data are presented.

Synthetic Aperture Radar (SAR) image despeckling is still an open issue. Several approaches have been proposed in the last decades. The recently proposed Non Local approaches are often considered as the state of art of SAR despeckling. The difference between the NL algorithms presented in literature is mainly related to the adopted distance metric between patches and on the rule used for averaging the selected pixels. Within this manuscript a new metric for selecting similar patches is presented. The metric is based on the statistical distribution of the complex noisy image. The Kolmogorov-Smirnov (KS) test is adopted to compare the statistical distribution and to select similar patches. The approach has been tested and validate on real data, showing interesting performances.

SAR tomography (TomoSAR) allows facing the problem related to the interference of coherent scatterers within the same pixels due to the occurrence of layover. Whereas, full imaging the continuous reflectivity profile along the elevation dimension is a typical framework to deal with the non-coherent volumetric scatterers in the forested area. Layover usually arises in volumetric scenario and leads to discontinuity in the reconstructed vertical reflectivity image. This paper aims to investigate the possibility of addressing layover issue in forested area by exploiting the unified non-local (NL) filtering of multi-baseline (MB) covariance matrix. To this aim, the performance of non-parametric Capon spectral estimation technique has been analyzed using the NL filtered MB covariance matrix and efficient vertical reflectivity profile reconstruction is demonstrated, which almost addressed the layover issues.

Automatic Target Recognition (ATR) aims at detecting the presence and at recognizing the typology and the orientation of targets within a scenario, by using an unsupervised approach. In Syntethic Aperture Radar imaging this turns to be a difficult task due to the specific characteristics of clutter and background noise. Within this manuscript a new two-steps ATR algorithm based on Kolmogorov-Smirnov test is presented. The method has been tested on real MSTAR datasets showing interesting performances.

Ground penetrating radar (GPR) represents a promising technology for the non-invasive exploration of soil and for the quantitative characterization and localization of buried objects. Unfortunately, this technique suffers for some limitations, among which the high number of data required for the processing of GPR information. In order to overcome this drawback, the paper proposes a mixed-norm approach based on the combination of compressive sensing (CS) theory and Wavelet decomposition basis to enhance signal processing and meantime to reduce the number of data required for the inversion procedure. The accuracy of the proposed approach is validated by means of a numerical analysis carried out in simplified two-dimensional (2D) scenarios.

Microwave imaging represent an emerging tool in the framework of noninvasive diagnostics, due to the potential advantages of providing quantitative characterizations of the inspected domains. In general, handling such a problem is not an easy task, but under some simplifying hypotheses it is possible to reduce the ill-posedness of the inverse problem and even to employ some linearization strategies. In this paper, a robust method for the quantitative imaging of weak buried objects in the framework of Ground Penetrating Radar applications by exploiting the theory of Compressed Sensing (CS) and Wavelet decomposition (WT) in a canonical two-dimensional configuration is proposed.

A novel approach for speckle reduction in SAR images is presented. An enhanced version of the Wiener Filter is proposed in order to locally adapt the filter characteristic to the image behavior, modelled by Markov Random Fields. First results on simulated and real data are reported.

Microwave imaging (MWI) techniques represent an assessed tool to face a lot of diagnostic problems in which a non-invasive analysis of an unaccessible area is required, since they allow to obtain qualitative and quantitative information about the nature of the targets embedded inside an imaging domain. In this communication, the authors propose an application of MWI techniques to a well-known aspect-limited configuration, that is the one proposed in Ground Penetrating Radar for demining application. More in details, we applied the theory of Compressive Sampling (CS) in order to improve the quality of recoveries and, in the meanwhile, reduce the number of data acquired and employed in a 3-D fashion in the case of low-moderate dielectric objects, and therefore the well-known Born Approximation has been exploited in the inversion algorithm.

In this paper the problem of despeckling Synthetic Aperture Radar images is addressed. An algorithm developed in the Bayesian estimation theory framework is presented. In particular, considering single look images, the algorithm applies an homomorphic filter followed by an Iterative Wiener filter to reduce the speckle. The proposed approach is tested on simulated and real X-band datasets showing interesting noise reduction capabilities.

It is well-known that in a canonical inverse scattering problem there is only a limited amount of independent data which is quantified by the degree of freedom of the problem one is dealing with. Of course, it is mandatory, in order to recover the signal in a proper way, to sample the data at least at Nyquist rate. However, there are some cases in which the number of independent data of a signal is much smaller than what its bandwidth seems to suggest, and in these situations the theory of Compressive Sensing may help to improve reconstruction capabilities without using so much information. In this framework, the following communication deals with a preliminary analysis of the solution of microwave imaging problems by exploiting the theory of Compressive Sensing in a linearized multiview-multistatic single-frequency approach.

A novel Phase Unwrapping (PhU) technique for InSAR interferometric stacks based on statistical estimation theory is presented. The approach is intended for exploiting both amplitude and phase of the acquired data in order to express the multi-baseline likelihood function without the assumption of independence among channels, i.e. by considering the full mutual correlation matrix. Moreover, the contextual information is adopted for regularizing the solution, obtaining a Maximum A Posteriori (MAP) estimator. First results on real datasets related to an urban scenario are presented, showing the interesting performances of the proposed method in terms of Digital Elevation Model (DEM) reconstruction.

Within this manuscript a novel technique for joint Digital Elevation Model (DEM) reconstruction and deformation estimation is presented. In particular, a Maximum A Posteriori (MAP) estimator that makes use of Gaussian Markov Random Fields (MRF) is proposed. The advantage of the approach, with respect to classical Permanent Scatterers (PS) based techniques, consists of its ability to evaluate the height and the deformation for all resolution cell across the scene, instead of only strong scatterers. Thus, the method is able to work also in natural scenarios, or in general when few PS are available. First results are presented on a simulated dataset with COSMO-SkyMed acquisition parameters.

Synthetic Aperture Radar (SAR) systems provide images with a resolution related to the transmitted signal and Doppler bandwidths. High resolution systems require large bandwidths, and then high sampling rates. Processing techniques based on Compressive Sensing (CS) can be applied for reducing sampling frequency and/or increasing spatial resolution. They are based on the assumption of a sparse reflectivity map of the imaged scene. The achievable performance depends on the degree of sparsity and on the level of noise affecting processed data. In this paper these issues are investigated by means of numerical experiments on simulated raw data for realistic SAR images.

In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of using the amplitude image, as in classical change detection approaches, the proposed technique uses the full complex image in a Markovian framework. The complex data are modeled using Markov random field hyperparameters, which are particular local parameters that take into account the spatial correlation between pixels. Starting from two data sets, the pre- and the postevent ones, the proposed algorithm, first, estimates the two hyperparameter maps and, then, compares the similarity between them. If a change occurs between the pre- and the postevent acquisitions, the statistical distribution of the hyperparameter maps will change. The maximum distance between the two obtained statistical distributions provides an index of changes. This sort of spatial correlation maps is computed using statistical estimation techniques, while the similarity comparison is computed using the two-step Kolmogorov-Smirnov statistic test. The algorithm is validated on simulated data and tested on real COSMO-SkyMed data acquired on the area of Naples, showing interesting and promising results.

In this manuscript a novel approach for SAR urban change detection is presented. Its peculiarity is its ability to detect the changes not directly from the measured amplitude data, but exploiting the whole complex image. In particular, the scene in modelled as a Local Gaussian Markov Random Field, and is described via the so called hyperparameters, which refers to the spatial correlation of pixels. By comparing such hyperparameters obtained from a pre-event and a post-event dataset, we can detect occurred changes. Results on real datasets show good detection accuracy together with very low false alarm rate.

Within this manuscript, a method able to extract lake shore from SAR complex data is proposed. Radar images have many advantages over optical images, such as no cloud coverage or solar illumination problem. The method models the observed scene as a Local Gaussian Markov Random Field and estimates in a Bayesian framework the so called hyperparameters. By proper thresholding such hyperparameters we can retrieve the lake shores. Exploiting SAR data from today available high resolution sensors, we can monitor lake shores with sub-meter precision. In the following, applications to two real data sets, showing the effectiveness of the method, are reported.

In this work an edge detector for man made structures within Spotlight Synthetic Aperture Radar (SAR) Images is proposed. The algorithm processes both real and imaginary parts of the data and so it is able to fully exploit the acquired image, being optimal from the information theory point of view. The detector has been tested on Cosmo-SKYMED (CSK) Spotlight image and compared to other single image edge detectors, showing interesting and promising results.

This communication deals with the solution of microwave imaging problems exploiting a Compressive Sampling (CS) based method, an emerging technique for data acquisition and signal recovery based on its property of requiring lower dimensional data. In particular, the inversion procedure was tested on the Contrast Source-Extended Born model. We also considered the classic Born inversion in order to remark the reconstruction ability of the proposed method.

In this work, a novel building edge detector which jointly exploits the full complex SAR image is proposed. The algorithm, starting from both amplitude and phase signals, detects building edges by estimating the spatial correlation between neighboring pixels. The observed scene is modeled using Markov Random Field theory. The proposed procedure is able to correctly identify the building and recover their shapes while maintaining a low false alarm rate, overcoming the limitation of existing techniques.

Recently Synthetic Aperture Radar Along Track Interferometric (ATI-SAR) has been successfully applied for traffic monitoring. In this work we treat the problem of detecting moving targets with unknown parameters (velocity and signal to clutter power ratio) from ATI-SAR images, exploiting a Generalized Likelihood Ratio Test (GLRT). We provide a closed form for the probability density function (pdf) of the log-likelihood ratio, for the threshold corresponding to an assigned probability of false alarm (PFA) and for the Receiver Operating Characteristic (ROC). The moving target detection capabilities are investigated on TerraSAR-X data provided by DLR.

The aim of this letter is to show how a joint adoption of a suitable regularization scheme and a proper rewriting of the traditional electromagnetic scattering equation allows introducing an interesting linear inversion tool which allows achieving nice reconstructions in many cases of practical interest. In particular, an innovative inversion approach which takes definite advantage from the joint use of the Contrast Source-Extended Born model and a Markov-random-field-based regularization scheme is proposed. Numerical examples, confirming accuracy usefulness, are reported and discussed.

In this paper, we investigate the robustness and the effectiveness of a microwave imaging technique, based on the Bayesian estimation theory, for the reconstruction of dielectric profiles. The method has been applied and validated on real experimental data. Our statistical-based inversion algorithm takes advantage of Bayesian regularization, which permits the inversion of a strongly nonlinear model using a Markov random field as an a priori statistical model of the unknown image. Such choice leads to a robust and effective nonlinear inversion method. The exhaustive analysis performed on the experimental data shows the good performance of the method.

This communication deals with the reconstruction of three-dimensional target from experimental multiple-frequency data measured in the anechoic chamber of the Institut Fresnel (Marseille, France). In order to take into account the random noise present in the experiments, a Bayesian approach is considered. In particular, the inversion procedure takes advantage from the joint use of the Contrast Source-Extended Born model and of a Markov Random Field regularization. We also considered a cost functional appropriately weighted by coefficients which change with the frequency, the incident angle and the receiving angle. In fact, each scattered field measurement is balanced with the noise disturbing the data.

In this paper a comparison between two techniques developed to recover layover solution in SAR images is presented. SAR Statistical Tomography and Compressive Sensing techniques are described and analyzed in order to provide a set of instruments for 3D SAR imaging able to tackle different scattering mechanisms in layover areas and to recover height reconstruction of an observed scene. The performances of the two techniques are compared on simulated data and some conclusions are drawn.

In this paper we analyze the performance of ground moving target detection by means of single-baseline and dual-baseline along track interferometric synthetic aperture radar (SAR) systems, obtained using a generalized likelihood ratio test (GRLT). Detection performance are evaluated in terms of probability of detection and probability of false alarm using data simulated with TerraSAR-X parameters.

The aim of this paper is the three dimensional reconstruction of urban areas using Very High Resolution (VHR) images. The proposed innovative approach for the three dimensional reconstruction is based on the joint exploitation of both amplitude and interferometric phase images of a multichannel SAR system. The information provided by the amplitude data is added to the 3D reconstruction chain, considering that in urban areas edges of amplitude image are likely also present in the interferometric phase one and conversely. Differently from other works present in literature, the proposed technique exploits the amplitude image, not only to improve the phase regularization, but also to improve the phase unwrapping step. The results will show the effectiveness of the method.

3D microwave tomography is an ill-posed and nonlinear inverse problem whose reconstruction performances may be impaired from the complexity of the scenario to be considered in the real world applications. The joint adoption of convenient regularization schemes and a suitable rewriting and linearization of the pertinent scattering model can allow to achieve satisfactory solutions in many cases of practical interest. In this communication, an innovative inversion approach which takes definite advantage from the joint use of the linearized contrast source-extended Born model (CS-EB), and from a Markov random field (MRF) based regularization scheme is proposed.

In this paper a methodology for the reconstruction of height profile of earth surface starting from layover affected Synthetic Apertuire Radar data is presented. The proposed approach is based on classical statistical estimation techniques, in particular using Maximum Likelihood Estimator, together with a Gaussian model for the point target response. Multi-channel configuration has been exploited in order to solve the solution ambiguity and to increase the reconstruction accuracy. The performances of the proposed estimator have been evaluated in comparison with the Cramer Rao Lower Bounds for the considered model, showing the effectiveness of the method. The height reconstruction procedure has been tested on a simulated realistic scenario, providing interesting and promising results.

In this paper we consider the problem of the detection of a ground moving target using Synthetic Aperture Radar Along Track Interferometric (AT- InSAR) systems. We propose a method exploiting a Generalized Likelihood Ratio Test (GRLT) and based on a Gaussian model for the target response. We also derive the log-likelihood ratio probability density function in closed form, both in the hypothesis of presence of target and absence of target. Numerical results based on simulated data are presented.

In this letter, a statistical-based approach to recover layover solution in synthetic aperture radar (SAR) images is proposed. The aim of this letter is to develop a methodology in order to separate different scattering contributions collapsed in a single SAR image pixel. After a brief discussion about layover, the proposed model is presented, followed by a discussion about achievable performances using Cramer-Rao lower bounds. In the final part of this letter, the performances of a maximum likelihood estimator are evaluated in a simulated data scenario, showing the effectiveness of the method.

In this letter, a methodology to overcome the layover problem and obtain the 3-D reconstruction of urban areas will be discussed. Interferometric synthetic aperture radar (SAR) (InSAR) systems allow the estimation of height profiles of the Earth surface, but in the case of urban scenarios, estimation becomes a hard task due to the presence of SAR geometrical distortions, with layover above all. First, the layover signal in InSAR images is investigated; then, a procedure to specifically manage layover areas is presented. The proposed method consists of introducing an auxiliary data exploitation, optical data or SAR shadowing, in the maximum a posteriori statistical estimation technique to improve the digital elevation model reconstruction, particularly on phase discontinuities. We test the method on simulated data, showing its effectiveness.

Markovian approaches have proven to be effective for solving the multichannel phase-unwrapping (PU) problem, particularly when dealing with noisy data and big discontinuities. This letter presents a Markovian approach to solve the PU problem based on a newapriorimodel, the total variation, and graph-cut-based optimization algorithms. The proposed method turns out to be fast, simple, and robust. Moreover, compared with other approaches, the proposed algorithm is able to unwrap and restore the solution at the same time, without any additional filtering. A set of experimental results on both simulated and real data illustrates the effectiveness of our approach.

In this letter, an analytical description of the 2-D and 3-D imaging of ground-based synthetic aperture radar data is given. The ability of the 3-D imaging to separate targets along the elevation direction will also be shown, thus allowing their complete localization in space. The validation of the proposed method is done by exploiting simulated data.

Interferometric synthetic aperture radar (InSAR) systems allow the estimation of the height profile of the Earth surface. When the height profile of the observed scene is characterized by high slopes or exhibits strong height discontinuities, the height reconstruction obtained from a single interferogram is ambiguous, since the solution of the estimation problem is not unique. To solve this ambiguity and restore the solution uniqueness, multiple interferograms, obtained with different baselines and/or with different frequencies, have to be used (multichannel InSAR). The height profile can then be estimated from multiple interferograms using maximum likelihood (ML) estimation techniques or by means of maximumaposteriori(MAP) estimation techniques, which take into account the relation between adjacent pixels. In this paper, the height estimation accuracy achievable with a given multibaseline interferometric configuration and using the aforementioned estimation techniques in terms of Cramer-Rao lower bound for the ML and of error lower bound for the MAP, is analyzed and discussed. It is shown that the MAP technique outperforms the ML one and that its attainable accuracy is not sensitive to the baselines choice, while mainly depends on the ground slopes.

We show that using the in-phase and quadrature components of the two acquired images in AT-InSAR systems produces an increasing of the detection probability of ground moving targets for constant false alarm rates respect to the detection performance obtained with AT-InSAR conventional systems using only phase information. In this paper, we consider a Gaussian model for the moving target response. The improved performances of the proposed method respect to the interferometric phase approach are showed with numerical experiments on simulated data, and varying the signal to clutter ratio (SCR) and the target radial velocity.

Interferometric SAR (InSAR) systems are able to estimate height profiles of the Earth surface. For the involved estimation problem, Maximum A Posteriori (MAP) statistical technique and Markov Random Field image models have been used, showing to be effective in case of multiple interferograms, obtained via different baselines/frequencies. In this paper, we are interested in the application of such estimation procedure in urban areas, where due to SAR geometry, many geometrical distortions appear. In particular, we focus on the layover problem. We present a procedure to manage layover areas, based on the optical and SAR data fusion. Moreover, we exploit the optical data to improve the a priori term of the MAP approach. We test the method on simulated data, showing the effectiveness of the method.

Ground-based SAR systems play a key role in active microwave remote sensing in many areas of environmental risk monitoring. Real time capability and flexibility make ground-based SAR suitable for monitoring in emergency cases, such as sudden landslide or volcanic activities. In this work we propose a 3D SAR imaging, for a ground-based stepped frequency radar, based on a tomographic technique. This method allows separate scattering mechanisms associated at target interfering in the same resolution cell.

Along-track interferometric synthetic aperture radar (AT-InSAR) can be used to estimate the radial velocity of ground moving targets, starting from interferometric phase measures. The estimation obtained from a single-phase interferogram suffers from ambiguities. To solve these problems, multichannel AT-InSAR systems are required. In this letter, we analyze the radial velocity maximum-likelihood estimation accuracy with respect to AT-InSAR system parameters, such as velocity values and different clutter and thermal noise levels. We consider two different models for the target response: a deterministic model and a zero-mean Gaussian model. The presented results show that AT-InSAR systems exhibit better estimation accuracies for low-velocity values (slow targets).

Multichannel interferometric synthetic aperture radar (InSAR) systems allow the estimation of the height profile of the Earth's surface, exploiting the availability of multiple radar acquisitions, obtained via different baselines/frequencies. Statistical approaches, in particular maximumaposterioritechnique and Markov random-field image models, can be exploited for such estimation problem, which proved to be effective. However, despite the particular solution method used, the problem with multichannel interferometry is that interferograms can be affected from the presence of undetermined phase offsets, which makes it difficult to get correct height estimation in any case. In this letter, we present a procedure to estimate these phase offsets using statistical estimation; we test the procedure on both simulated and real data. For the latter, we show how an optimal estimation of the phase offsets can be used to improve the resolution of an available Shuttle Radar Topography Mission digital elevation model. The obtained results prove the effectiveness of the method and assess the overall quality of the height estimation procedure.

Interferometric SAR (InSAR) systems are able to estimate height profiles of the Earth surface. For the involved estimation problem, Maximum A Posteriori (MAP) statistical technique and Markov Random Field image models have been used, showing to be effective in case of multiple interferograms, obtained via different baselines/frequencies. In this paper, we face the problem of unknown phase offsets presence affecting real interferograms, which makes impossible to retrieve correct height estimations. We present a procedure to estimate these offset values, based on statistical estimation and we test it both on simulated and real data, showing the effectiveness of the method.

Along-Track Interferometric SAR (AT-InSAR) can be used to measure moving ground target velocity. In this paper we have investigated how, exploiting multi-channel interferograms, the velocity estimation accuracy depends on the statistical model assumed for the interferometric phase. We have verified the different accuracies in the estimation of the target velocity by investigating the changes in the Cramer-Rao bounds with respect to two different target image statistical model, a deterministic and a Gaussian model. Generative and estimation model have been tested with numerical experiments on simulated data.

Interferometric SAR (InSAR) systems allow the estimation of the height profile of the Earth surface. Maximum Likelihood (ML) and Maximum A Posteriori (MAP) statistical techniques have shown to be effective for such problem if multiple interferograms, obtained with different baselines and/or with different frequencies, are used (multi-channel InSAR). In this paper, we evaluate the reconstruction performance of the considered ML and MAP statistical height estimation methods in terms of the Cramer-Rao Lower Bounds (CRLB) of the estimated height values.

In this paper we introduce an algorithm for velocity estimation of a ground moving point target using a multi-channel along-track interferometry (MC-ATI) system. The presented results are relative to a multi frequency system, but the algorithm can be used also for a multi-baseline one. The performance of the system is evaluated by presenting also the probabilities of detection.

SAR images are affected by speckle that affects radiometric resolution and class discrimination capabilities. Recently, different speckle reduction techniques based on maximum a posteriori (MAP) estimation have been proven to have very good performances. These techniques are based on the introduction of an a priori statistical model of the speckle free image to be estimated. We propose a MAP method using more than one sub-band filtered intensity images and a Markov random field (MRF) a priori model. The method has been experimented on simulated and real images

In this paper we present some preliminary results of the application on real data of a statistical method to solve the height estimation problem in Interferometric Synthetic Aperture Radar (InSAR). The method is based on maximum a posteriori (MAP) estimation and Markov Random Fields (MRF) image modeling, and makes use of multifrequency/baseline SAR raw data. The real data set is acquired by a Ground-Based SAR (GB-SAR) interferometer based on the LiSA technology

We present a statistical method to solve the height estimation problem in interferometric synthetic aperture radar (InSAR) applications. It is based on the use of multifrequency SAR raw datasets obtained by partitioning in subbands the available raw data spectrum, and on a Bayesian estimator using Markov random fields to model the a priori distribution of the unknown images. The method allows recovering topographic profiles affected by strong height discontinuities and allows to perform efficient noise rejections.

We address the problem of non-linear microwave imaging for the reconstruction of dielectric profiles. We propose a statistical based inversion algorithm, adopting the Bayesian (MAP) framework and a complex Gaussian MRF model for the image. The use of statistical algorithms for the estimation of the complex MRF parameter leads to a robust and effective non-linear inversion method. Some experiments on real data are able to show the good performance of the method.

We show the information theoretic performance of two maximum likelihood techniques to solve the problem of phase unwrapping in SAR interferometry. Bias and minimum variance of the estimators, in terms of the Cramer Rao lower bounds, are computed.

The application of a maximum a posteriori estimation method for microwave imaging that makes use of a Markov random field (MRF) a priori statistical model is presented. In particular, the MRF family adopted is generalized for complex profiles, characterized by a quadratic energy function and "modified" such to make it possible to statistically represent spatial interactions between real and imaginary parts. Thanks to its peculiarities, the use of this approach simultaneously favors in many cases well posedness and robustness against local minima occurrence, as well as high quality in the reconstructed images. Numerical results show the performance of the method.

A technique for compressing synthetic aperture radar raw data using multiresolution representations and subband coding is considered. In particular, we present the performance of a transform coding compression method using wavelet basis, coupled with a threshold quantizer optimized for Gaussian statistics, as well as a proper subband bit allocation strategy. The performances achieved in terms of bit rate reduction and certain quality parameters computed on the images obtained by compressed data have been evaluated. These show an increased performance in the compression method with respect to conventional methods, albeit with a slightly increased complexity in the algorithm implementation.

The application of a Markov random fields (MRF) based maximum a posteriori (MAP) estimation method for microwave imaging is presented in this paper. The adopted MRF family is the so-called Gaussian-MRF (GMRF), whose energy function is quadratic. In order to implement the MAP estimation, first, the MRF hyperparameters are estimated by means of the expectation-maximization (EM) algorithm, extended in this case to complex and nonhomogeneous images. Then, it is implemented by minimizing a cost function whose gradient is fully analytically evaluated. Thanks to the quadratic nature of the energy function of the MRF, well posedness and efficiency of the proposed method can be simultaneously guaranteed. Numerical results, also performed on real data, show the good performance of the method, also when compared with conventional techniques like Tikhonov regularization.

The application of a Markov random field (MRF) based maximum a posteriori (MAP) estimation method for microwave imaging, in a non-linear framework, is presented. The adopted MRF family is characterized by a quadratic energy function, and is "modified" such that it statistically represents complex profiles. This approach simultaneously allows, in many cases, well posedness and robustness against local minima occurrence.

A multi-frequencies maximum a posteriori (MAP) estimation of height profiles, from InSAR data, is presented in this paper. A quadratic MRF model is adopted to exploit a-priori information about the unknown image; the hyperparameter estimation, performed on a local basis, provides a powerful representation model for realistic height surfaces. The resulting MAP estimation is efficiently performed by a Metropolis version of the simulated annealing algorithm, and is able to reconstruct very discontinuous profiles.

Multiple SAR data sets can be exploited to improve the system range resolution. Obtainable resolution improvement can be impaired by a coherence loss among the different data acquisitions. The effect of different decorrelation factors on the attainable resolution is investigated by numerical simulation.

In this paper, a technique that is able to reconstruct highly sloped and discontinuous terrain height profiles, starting from multifrequency wrapped phase acquired by interferometric synthetic aperture radar (SAR) systems, is presented. We propose an innovative unwrapping method, based on a maximum likelihood estimation technique, which uses multifrequency independent phase data, obtained by filtering the interferometric SAR raw data pair through nonoverlapping band-pass filters, and approximating the unknown surface by means of local planes. Since the method does not exploit the phase gradient, it assures the uniqueness of the solution, even in the case of highly sloped or piecewise continuous elevation patterns with strong discontinuities.

The purpose of the paper is to investigate the possibility of reconstructing high slope and discontinuous terrain height profiles, starting from more than one wrapped interferometric phase signals obtained at different working frequencies from interferometric SAR systems. We propose an unwrapping method, based on a maximum likelihood estimation technique, that approximates the unknown surface by means of local planes. Since it does not exploit at all the phase gradient, it assures the uniqueness of the solution, also in the case of piece-wise continuous elevation patterns with strong discontinuities.

We present a phase unwrapping method using a maximum likelihood estimation technique together with frequency diversity information to reconstruct highly discontinuous ground elevation profiles. Frequency diversity can be obtained by considering the interferograms obtained by different couples of subband images.

In inverse scattering problems, only a limited amount of independent data is actually available whenever the finite accuracy of the measurement set up is taken into account. The authors deal with the problem of quantifying such an amount in the subsurface sensing case. In particular, an alternative formulation of the problem is given which also allows one to understand how to dimensionate the measurement setup in an optimal fashion. Analytical results are reported for the case of a lossless soil, while a numerical study is carried out in the general case. By relying on the same formulation and tools, the authors also discuss the kind of unknown profiles that can actually be retrieved. In particular, it is shown that the class of retrievable functions exhibits intrinsic multiresolution features. This suggests that adoption of wavelet expansions to represent the unknown function may enhance the reconstruction capabilities. Numerical examples support this conclusion.

The reliability of a recently introduced nonlinear estimation method for tomographic imaging is discussed in full detail. It is shown how a proper choice of the functional spaces to which unknown quantities belong and the exploitation of the expected properties of the object under test and of all the available a priori information positively affect robustness against false solutions of the inversion procedure. The developed arguments allow the authors to understand causes of possible false solutions, suggesting possible countermeasures. In particular, it is shown how the proposed approach allows the authors to achieve accurate and reliable reconstructions in a set of cases larger than the range of applicability of other "false solutions free" approaches. Numerical analyses confirm the validity of the approach and the effectiveness of developed inversion procedures through practical examples.

The aim of this paper is twofold. First, why and how exploitation of multifrequency information is of great usefulness in inverse scattering problems is discussed. Second, three different solution strategies, all based on a recently introduced approach, are presented, discussed, and compared in the actual case of noise affected data in the two-dimensional (2D) scalar case. The first one is a (nonlinear) frequency-hopping technique, which favorably compares with approaches of the same kind that use linear inversion steps. As a second approach, the contemporary use of the different frequencies data is considered. Contrary to common assumptions, it is shown that such an approach may perform better than the previous one, the different performances between the two being dictated by the spatial frequency content of the unknown contrast. Finally, a hybrid and, to the best of the authors' knowledge, novel strategy, which exploits the advantages of the the previous ones, is introduced and discussed.

A non-linear estimation approach to reconstructing the space-varying permittivity profile of unknown objects is considered. It is shown that the equations governing the scattering are described by a bilinear model, and can be approximated into finite dimensional spaces on the basis of the finite degrees of freedom of data, and that one can not expect to reconstruct an arbitrary function from a finite number of independent equations. As a consequence, a discrete model, well suited to numerical inversion, can be developed. The bilinear nature of the equations, and the suitable choice of the unknowns allow for the functional adopted in the estimation to be minimized in an accurate manner. Numerical experiments validate the effectiveness of the proposed approach.

Processing of one bit coded synthetic aperture radar (SAR) signals is considered. One bit coding amounts to retaining only the information about the sign of the signals (signum coding or SC). Theory shows that the image obtained is proportional to that obtained by conventionally quantized data. Possible discrepancies depends on the frequency overlapping among the higher order harmonics generated by the SC operation. To improve the image quality, a sampling rate increase with respect to one consistent with the original signal bandwidth has to be used. Some image quality parameters are also evaluated on actual SAR images.

This paper is concerned about the generation of interferometric phase patterns using synthetic aperture radar (SAR) images obtained by processing the raw data and reference function both quantized at one bit (Signum Coded). Such processing technique involves one-bit coded (i.e., binary) sequences, and can be efficiently implemented in real time using very simple and low cost hardware. It is shown that the proposed SC processing technique preserves, besides the image intensities, also interferometric phase patterns, before and after phase unwrapping. To test the performance of the proposed technique, experiments have been carried out on real data relative to the ERS-1 mission. Quantitative comparison between the results of conventional and SC processing clearly show that the presented method can be used for quick-look DEMs generation. Moreover, in accordance with the SC-SAR theory, an upsampling has also been performed on the signals to be processed to obtain higher quality patterns. This produce a noticeable improvement of the obtained results, so that the SC techniques can be considered a valid alternative to the conventional ones, still preserving the advantages in terms of real time.

A nonlinear estimation approach to solving the inverse scattering problem, and reconstructing the space-varying complex permittivity of unknown objects is considered. The bilinear operator equations governing the scattering are approximated into finite dimensional spaces on the basis of the finite degrees of freedom of data, and on the simple concept that one cannot expect to reconstruct an arbitrary function from a finite number of independent equations. As a consequence, a discrete model, well suited to numerical inversion, is developed. The particular bilinear nature of the equations, and a suitable choice of contrast and field unknowns allows the functional adopted in the estimation to be minimized in an accurate and numerically efficient manner. Numerical experiments show how the method is capable, when a proper number of searched unknowns is adopted, to manage the possible convergence to local minima (which is a typical question in nonlinear inverse problems), and validate the effectiveness of the proposed approach.