Abstract—Coastal water quality monitoring is crucial for environmental sustainability and public health. This work introduces a very cutting-edge methodology, using ΦSat-2 multispectral data and quanvolutional neural networks to explore quantum-enhanced machine learning for water contaminant assessment. By integrating quantum preprocessing into a classical regression model, it is possible to achieve a significant reduction in model parameters while maintaining high predictive accuracy. Additionally, this work introduces an innovative dataset that integrates simulated ΦSat-2 spectral data with Copernicus Marine Service bio-geochemical products, ensuring a strong alignment between satellite observations and reference turbidity measurements. Our results show that quantum models use up to 98% fewer parameters than their classical counterparts, while achieving a 6.9% improvement in the Pearson correlation coefficient between the ΦSat-2 pre-processed bands and the ground-truth turbidity values, compared to the case without quantum pre-processing. Additionally, the Root Mean Square Error (RMSE) improves by 7.3% over the classical baseline. These findings highlight the potential of quantum-assisted remote sensing to enable more efficient and scalable analysis of large-scale water contaminant data, paving the way for advanced big data approaches in water quality monitoring.

In recent years, generalized likelihood ratio test (GLRT) scatterers’ detection in the context of synthetic aperture radar tomography (TomoSAR) has gained great interest from the remote sensing scientific community. This is due to its effectiveness in identifying scatterers within each single azimuth–range resolution cell, particularly in urban areas. The multilook GLRT (M-GLRT) variant offers more satisfactory results at the expense of spatial resolution deterioration, by jointly exploiting the neighboring information of the pixel to be reconstructed. In this context, coherent and incoherent formulations can be adopted. The former provides better performance in the assumption of constant reflectivity of the pixels in the considered neighborhood, while the latter is much more robust with respect to the violation of this assumption. In this article, an adaptive formulation of the coherent and incoherent GLRT is presented with the aim of improving scatterers’ detection and their height estimation. The method is based on an adaptive window setting, to select adjacent pixels with similar height and reflectivity characteristics. A detailed study and analysis of the proposed adaptive coherent M-GLRT (ACM-GLRT) detector has been conducted and validated through simulations along with comparison to both standard and adaptive formulations of incoherent M-GLRT. Experimental findings from a real dataset acquired by the German TerraSAR-X (TSX) over the city of Naples (Italy) demonstrate the performance improvement of our proposed approach.

The reliability of Tomographic SAR (TomoSAR) products depends on the quality of complex-valued tomographic interferograms. The latters are unfortunately affected by noise from various sources. In order to reduce their impact and hence improve the outcome, filtering methods can be implemented at different levels of TomoSAR process. In this paper, we propose the application of a contextual denoising approach in transformed domains, based on Subbands decomposition and non-linear weighting, with the aim to study its influence on TomosAR height and deformation velocity estimation using Generalized Likelihood Ratio Test detection. Both spatial and spatiotemporal arrangements of overlapping blocks are considered in wavelet domains. The non-linear filtering parameter is estimated from the coherence and/or pseudo-correlation indicators. In order to show the effectiveness of the approach, the obtained findings have been compared to the state-of-the-art methods namely Goldstein and Baran filters. The assessment of the results with respect to denoising and TomoSAR evaluation metrics was carried out using both simulated and real data acquired by TerraSAR-X satellite over the city of Naples.

Conventional geodetic methods rely on point measurements, which have drawbacks in detecting and tracking geologic disasters at specific locations. In this study, the estimation of ground surface deformation in economically important urban regions of Pakistan's southern coast from 2017 to 2022 is performed using interferometric SAR technique. Vertical displacement which was used to investigate the potential correlation with the most effective causative parameters of deformation. The densely populated areas of the study area experience annual subsidence of 130 mm, and the western less populated region experiences an uplift of 70 mm annually. The densely populated areas of the study area experience an annual subsidence of 130 mm, and the less populated western region experiences an uplift of 70 mm annually. Land deformation varies along the coast of the study area, where the eastern region is highly reclaimed and is affected by erosion. The results obtained on a Sentinel-1 SAR dataset indicate that there is significant subsidence in the major urban districts. A linear PS-InSAR approach is required to address the ground movement activities acutely, and it will make it possible to plan surface infrastructure and handle issues brought on by subsidence more effectively. This information is crucial for coastal management, hazard assessment, and planning sustainable development in the region.

Synthetic Aperture Radar Tomography has been an active research field for the estimation of both artificial structures and forest heights. Most spectral analysis approaches explore the covariance matrix (CM) to provide accurate reflectivity profiles. In practical situations, the statistical CM is unknown, hence several methods replace it with the sampling CM. Unfortunately, the latter does not always meet the application requirements. At our end, we propose to apply the Expectation Maximization algorithm to iteratively estimate the pseudo spectrum and then update the CM. The impact the proposed method has on height estimation over vegetated areas is carried out. Experimental results on a real dataset acquired by an airborne system show the effectiveness of the Expectation Maximization estimator in terms of ground and canopy discrimination for each polarization channel.

Tomatoes are a globally important vegetable crop, but diseases can have devastating consequences for tomato plants, highlighting the need for prompt identification and treatment of these infections. A variety of machine learning algorithms and Convolutional Neural Network (CNN) models have been proposed in the literature for detecting tomato plant diseases. CNN models leverage the power of deep learning and neural networks. This paper introduces a simplified light weight CNN model comprising ten hidden layers, which surpasses conventional machine learning techniques and pre-trained achieving an impressive accuracy of 99.7 percent. We have collected different types of images samples from Kaggle with the range from 10000-40000 images. The model has been trained and test using different data and the performance was closely examined to make sure the collected data correct. The CNN model after quantization has been implemented through Ryze Tech Tello Mini Drone Quadcopter for the testing real time images.

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.

Monitoring water contaminants is of paramount importance, ensuring public health and environmental well-being. Turbidity, a key parameter, poses a significant problem, affecting water quality. Its accurate assessment is crucial for safeguarding ecosystems and human consumption, demanding meticulous attention and action. For this, our study pioneers a novel approach to monitor the Turbidity contaminant, integrating CatBoost Machine Learning (ML) with high-resolution data from Sentinel-2 Level-2A. Traditional methods are labor-intensive while CatBoost offers an efficient solution, excelling in predictive accuracy. Leveraging atmospherically corrected Sentinel-2 data through the Google Earth Engine (GEE), our study contributes to scalable and precise Turbidity monitoring. A specific tabular dataset derived from Hong Kong contaminants monitoring stations enriches our study, providing region-specific insights. Results showcase the viability of this integrated approach, laying the foundation for adopting advanced techniques in global water quality management.

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.

The detection of multiple scatterers within each resolution cell is an open research subject in synthetic aperture radar (SAR) tomography (TomoSAR). For over a decade, the generalized likelihood ratio test (GLRT) detector has been implemented along with its variants, allowing the generation of height maps and 3-D point clouds with good precision. However, they are limited by the grid search during the optimization of the maximum likelihood function. In order to mitigate this, we propose a gridless version of GLRT where the particle swarm optimization (PSO) method is used to locate the minima. The conducted analysis of the proposed detector with respect to the state-of-the-art methods behavior on simulated and real datasets proved the effectiveness of PSO-GLRT in terms of height accuracy and computational cost. The evaluation metrics, root-mean-square error (RMSE), accuracy, and completeness, have been used as a quantitative improvement indicator for estimated height assessment.

The reconstruction of high-elevation natural and artificial structures through Synthetic Aperture Radar (SAR) tomography has been an active research topic owing to its significance in various earth science applications. However, the complexity of this task arises from inaccuracies in the estimated reconstruction, attributed to factors such as low signal-to-noise ratios, decorrelations, few and uneven measurements, and overlapping scatterers. The utilization of iterative spectral estimation methods has been demonstrated to be beneficial in addressing some of these inaccuracies. Thus, selecting the best method within this class constitutes a challenge. In this context, our letter aims to propose a generalized formula linking the maximum likelihood-based iterative methods via a regularization parameter. The behavior of the latter is analyzed for several values in order to unveil the potential of the proposed approach in achieving a balance between noise reduction and detection performance. The experimental study has been conducted on simulated and real SAR data acquired by airborne and spaceborne systems covering tropical forest and build-up areas. The obtained results show the effectiveness and performance of the optimal regularization parameter to eliminate noise while preserving scatterers’ contribution.

Surface deformation caused by an earthquake is crucial for a better understanding of the development of geological structures and seismic hazards in an active tectonic area. On September 24, 2019, an earthquake with a magnitude of 5.6 Mw and a depth of 10 km struck Mirpur, Pakistan, causing significant damage. The study area is already facing numerous problems due to natural hazards, and the additional surface deformations caused by this earthquake have further increased its vulnerability. The objective of this study was to estimate the surface deformation associated with the earthquake. InSAR analysis was applied to 10 Sentinel-1A SAR images captured between July 30, 2019, and October 22, 2019, resulting in the generation of 7 interferograms that provided information on ground displacement caused by the earthquake. The estimated deformation range showed approximately -8 cm of subsidence and a 20 cm uplift of the surface along the line of sight (LOS). Vertical deformation was also estimated to range from -3 cm to 17 cm.

Multi-baseline multi-temporal Synthetic Aperture Radar (SAR) techniques such as SAR Tomography (TomoSAR) are affected by different forms and sources of noise. Its presence in complex-valued interferograms alters the reliability and accuracy of height estimation results. Thus, the key challenge of TomoSAR is to identify scatterers interfering within the same resolution cell. To this aim, we propose a spectral contextual filtering method based on subbands decomposition to reduce noise influence and improve the quality of the interferometric product for TomoSAR application. The impact of the proposed pre-processing approach on the Generalized Likelihood Ratio Test-based detection is carried out. Experimental results on data acquired by TerraSAR-X sensor show the performances of the denoising method to increase the detection capabilities.

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 tomography (TomoSAR) has recently received particular interest from the remote-sensing community, due to its ability to provide 3-D reconstructions of environments with complex structures. Unfortunately, different forms of decorrelations and processing errors affect the quality of the resulting 2-D/3-D images. One way to cope with the impact of these nuisances is to apply appropriate filtering to the interferometric data stack as a preprocessing step. The first obstacle to be dealt with, especially in urban areas, is to define a filter whose parameters have to be set in such a way as to improve smoothing capabilities while preserving edges. To this aim, the main objective of this article is twofold: 1) the application of a spatio-temporal contextual filter whose parameters depend on 3-D quality indicators of the multibaseline interferometric image stack and 2) evaluation of the denoising effect on the application of nonparametric spectral estimation and detection algorithms. For that, we consider several quantitative metrics to assess, on the one hand, the filtering performances, and, on the other hand, its impact on the reflectivity function recovered from conventional tomographic inversion and detection methods. Experimental results from a set of TerraSAR-X (TSX) images highlight the efficiency of the filtering process by improving the scatterers’ detection and height localization, of a man-made structure.

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.

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.

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.

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.

In this paper a Polarimetric SAR Tomographic (Pol-TomoSAR) technique, exploiting the local contextual information in small neighborhoods surrounding each range-azimuth pixel, is applied to fully polarimetric multi-baseline images of an urban area. In particular, the considered technique is based on the assumption that the pixels belonging to a neighborhood are located on a locally planar surface, whose slopes can be estimated for each range-azimuth pixel from the surrounding neighboring data. This assumption well adapts to urban areas, where the height profile can be well approximated by planes. In this way, the correlation among the heights of neighboring pixels can be taken into account for improving the accuracy of the height profile reconstruction. Preliminary results on fully polarization SAR data are presented.

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.

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.

Synthetic Aperture Radar (SAR) tomographic techniques enable the reconstruction of the scene scattering structure along the vertical direction and can provide the temporal evolution of a cloud of reliable points located in the 3D space. The use of Generalized Likelihood Ratio Test approaches have been shown to be effective in selecting reliable multiple scatterers. Recently regularized tomographic methods have been proposed for increasing the density of the recovered scatterers in urban environments. This paper discusses the differences between these two approaches and performs a comparison of reconstruction results obtained from a stack of TerraSAR-X images, in a region of interest located in the city of Paris, France.

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.

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 this paper, the potential of a deep learning approach for SAR tomography (TomoSAR) is investigated. TomoSAR is a powerful technique that allows the 3D reconstruction of objects lying on the Earth surface, by separating multiple scatterers with different elevations laying in the same range-azimuth resolution cell. In urban applications, the number of interfering scatterers is typically very small, so that the reconstruction of the elevation reflectivity profile can be faced as a statistical detection problem. Detection performance depends on how well the adopted statistical model fits to the observed scene. For complex urban scenarios this issue can greatly impair achievable accuracy of results. Then, we propose to exploit the neural networks' capabilities to learn the data generative model, in order to face the problem of signal model inaccuracies. In particular, in the assumption of a single scatterer, a neural network can be trained to solve a simple classification problem. Results on simulated and real data are presented.

Temporal decorrelation is one of the major problems in synthetic aperture radar (SAR) tomography (TomoSAR) of a natural environment that leads to blurring and spreading in focused image space. In the context of spatiotemporal focusing using the multi-temporal multi-baseline (MB) SAR data, a model-based differential TomoSAR is employed. Along this and with the aim of temporal decorrelation-robust focusing, a differential tomography framework based on generalized Capon estimator is investigated. The method can cope with temporal decorrelation of the distributed environment by spatiotemporal focusing with optimal bandwidth of the distributed signal. In addition, the method employs an additional parameter for coherence channel balancing in the model of generalized Capon that benefits from it in characterizing the spatiotemporal backscattering by mitigating the inconsistency between channels. The analysis is performed with a realistic simulation of temporal decorrelation in the presence of different decorrelation sources and taking into account the dependence on the vertical structure of the forested area. Effectiveness of the proposed framework has been assessed on both simulated and real data sets by evaluation and characterization of the canopy and under foliage ground in terms of deviation between the estimated covariance matrix and one of the generalized TomoSAR models.

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.

This letter analyzes the impact of the accuracy of estimation of the data covariance matrix in synthetic aperture radar tomography of vegetated areas. The characterization of volumetric areas requires a robust estimation of the covariance matrix, which is usually performed by means of an averaging operation of neighboring pixels. In this letter, a different approach, based on the use of local and nonlocal (NL) neighborhoods of pixels, is evaluated. The analysis considers the quality of the reconstructed vertical profile obtained using both single and fully polarimetric multibaseline (MB) data. In the case of fully polarimetric data, two procedures for obtaining the vertical reconstruction are analyzed: the sum of Kronecker product decomposition of the covariance matrix and a procedure based on the full-rank estimation of a 3-D coherence matrix. The analysis of the impact of NL technique on the robust estimation of covariance matrix and on the capability of separating the interfering signals is performed using simulated and P-band real MB data.

This paper deals with the compensation of phase miscalibration in the general context of tomographic synthetic aperture radar image focusing. Phase errors are typically independent of one acquisition to the other, thus leading to a spreading and defocusing in the multidimensional (3-D, 4-D, and 5-D) imaging space. Coping with this problem in presence of volumetric scattering is generally a complex issue. In this paper, we consider the approach for phase calibration characterized by the advantage, with respect to classical phase calibration algorithms, of not requiring either the identification of a reference target or specific assumptions about the unknown phase function, or a priori information about the terrain topography. The novelty of the proposed phase miscalibration estimation and compensation method is related to its ability to avoid unwanted and uncontrollable vertical shifts in the focused image. The estimation of the calibration phase is performed by optimizing the contrast or the entropy of the vertical profile with the constraint of a zero phase derivative. Such a constraint preserves the output height distribution. Experimental results of simulated and real data are included to demonstrate the effectiveness of the proposed method.

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.

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.

In the last decade, Persistent Scatterer Interferometry (PSI) and SAR tomography (TomoSAR) have been used for reconstructing the elevation profile of a scene, starting from a set of co-registered Synthetic Aperture Radar (SAR) images. The possible advantage of TomoSAR over classical interferometric methods consists in the potential capability of improving the detection of single scatterers presenting stable proprieties over time (Persistent Scatterers or PS), as well as to enable the detection of multiple scatterers interfering within the same range-azimuth resolution cell. In urban environment, when only single dominant scatterers are present in each range-azimuth resolution cell, both methods can be exploited to estimate the altitude, deformation rate and thermal expansion of a subset of reliable scatterers, which are selected on the basis of different criteria. This paper is focused on a performance analysis of the two class of methods, using the results obtained in urban environment on simulated and real TerraSAR-X data. A concise description of both techniques, along with a discussion on their potential capabilities in selecting the most reliable scatterers, is given.

Sparse Representation and Compressive Sensing (CS) theory has gained an increasing interest during the last years in many application fields, including SAR tomography. The latter offers the possibility to perform a focusing, beyond the classical 2D (azimuth-range) domain, along other dimensions: f.i., elevation and velocity. The literature, however, lacks of an assessment of the improvement of CS over classical point cloud detection schemes based on the Generalized Likelihood Ratio test which, in the simplest form, use basic beamforming (matched filter) detection based schemes. This work aims to provide a contribution along this line.

One of the main challenges in Interferometric Synthetic Aperture Radar (SAR) is the accurate height reconstruction of the observed scene. Recently, approaches based on Extended Kalman Filter (EKF) have been proposed. Most of them are based on the hypothesis of height profile continuity. Such condition greatly reduces their applicability, being only valid for particular scenarios. Within this paper, we present a novel Kalman-based height reconstruction approach, specifically designed to work with multichannel data related to any type of scenario, both smooth or sharp. The novelty of the technique consists in its ability in detecting and correctly handling sharp height discontinuities while regularizing smooth areas. The approach is able to maintain the high computational efficiency typical of EKF and to work in an almost unsupervised way. The methodology has been tested and validated on both simulated and real X-band (TerraSAR-X and COSMO-SkyMed) high-resolution data sets. Reported results are encouraging and interesting, showing the correctness and the validity of the proposed approach.

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.

Detection of multiple scatterers for localizing the targets is one of the key issues in SAR tomography. Recently, a Generalized Likelihood Ratio Test based on support estimation (Sup-GLRT) [10] has been presented. This test exhibits a high computational complexity. In this paper a modified approach for reducing computational complexity (Fast-Sup-GLRT) is proposed. The prime objective is to analyze the performance of Fast-Sup-GLRT detector in terms of implementation and computational complexity. For an assigned probability of false alarm and with a given number of acquisitions the performance is analyzed and compared with the one obtained with the Sup-GLRT. Results on simulated and real HighRes SpotLight TerraSAR-X data are presented.

In this paper we extend the Fast-Sup-GLRT Detector, designed for SAR tomography (3D-SAR), to the detection of multiple scatterers that can exhibit time deformation. It assumes at most Kmaxdifferent scatterers in the same range-azimuth resolution cell with a phase model that takes into account phase variations due to the deformation and/or dilation of the scatterer(s). Results on simulated and real data are presented to validate the proposed approach.

Within this manuscript a novel Multi-channel InSAR phase unwrapping method is proposed. The approach implements an Extended Kalman Filter for jointly unwrap the phase and regularize the result. The novelty of the methodology consists in the probabilistic data association step that has been implemented in order to improve the robustness of EKF for PhU. Encouraging results on simulated dataset are reported.

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.

In this paper we focus on the detection of single and double scatterers in SAR tomography. In particular, the performance of a support based Generalized Likelihood Ratio Test (GLRT) approach is analyzed, using TerraSAR-X system parameters, with particular reference to the elevation resolution achievable for an assigned probability of false alarm and with a given number of acquisitions. Results on simulated and real data are presented.

In this paper we investigate SAR image compression based on sparse representation. Two approaches are considered: the first one is based on the use of an Overcomplete ICA transform coding method, the second one is based on Compressive Sensing (CS). In both cases an Overcomplete ICA representation is used as sparse representation, but while in the first case the significant overcomplete ICA coefficients are coded using an optimal entropy constrained threshold quantizer, in the latter case a reduced number of measurements obtained combining the SAR image pixels through a random measurement matrix are directly coded. Numerical results on TerraSAR-X images are presented.

In this paper, the detection of single persistent scatterers, exploiting multi-pass interferometric SAR acquisitions, is addressed. A Generalized Likelihood Ratio Test (GLRT) approach that takes into account the presence of correlated clutter is proposed and compared with the case in which clutter is assumed to be white. To assess the performance of the proposed approach, detection results obtained on simulated and real data are presented.

The availability of high-resolution along-track interferometric synthetic aperture radar (ATI-SAR) data with large coverage, such as TerraSAR-X (TSX) data, motivates spaceborne ground moving target detection as an attractive alternative to conventional traffic data acquisition. In this paper, a performance analysis of ground moving targets detection by means of ATI-SAR systems and using a statistical approach is carried out on both simulated and real data. A Gaussian clutter model and a deterministic target response have been assumed. The receiver operating characteristic for the likelihood ratio test (LRT), which can be assumed as a reference best performance case, has been expressed in closed form and has been related to the deflection values, which can be exploited for assessing the improvements in the detection probability with a constant false-alarm rate. For practical applications, the performance of a generalized LRT (GLRT) has been investigated. The analysis carried out on simulated data revealed that the detection results achieved using a GLRT based on a deterministic target model are comparable with those obtained using a GLRT based on a Gaussian target model and are not significantly worse than the theoretical performance of the LRT. Finally, ground moving target detection results on TSX real data are showed.

Phase Unwrapping (PhU) is the operation needed in order to generate 3-Dimensional height profile starting from Synthetic Aperture Radar (SAR) data acquired in the interferometric configuration. Due to the presence of height discontinuities (buildings) and due to the presence of noise, PhU becomes a difficult task to face in urban scenarios. In this paper we propose a new methodology especially thought for generating 3D height profiles of urban areas when multiple interferograms are available. The technique is based on the use of Kalman Filter in its Extended form (Extended Kalman Filter - EKF). The main peculiarity of the technique is the introduction of a specific step in the PhU procedure able to identify the possible height discontinuities and to consequently adapt the EKF behaviour. The algorithm is validated on both simulated and real cases.

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 paper we investigate the application of a GLRT detector of multiple scatterers in SAR tomography, exploiting the sparsity assumption of the signal in the elevation direction. The GLRT approach aims at estimating the signal support (positions of the samples different from zero of the unknown sparse signal) that best matches to the data for detecting and positioning multiple scatterers lying in the same range-azimuth resolution cell.

SAR tomography allows the three-dimensional (3D) reconstruction of the reflectivity profile of the observed scene on the ground. It is based on the acquisition of several images of the same scene, collected with different view angles along slightly different orbits of the SAR platform. The spatial resolution in the elevation direction obtained with conventional Fourier-type techniques is related to the overall baseline extent of the acquisition orbits. Recently a tomographic technique denoted as Compressive Sampling Tomography (CST) has been introduced. It allows a drastic reduction of the number of acquisitions required and attains an increased resolution in the elevation direction. It exploits the sparsity property of the reflectivity profile in the elevation direction. In this paper some results obtained by applying CST to COSMO-Skymed real data are presented.

In this paper we assess the ground moving target detection performance based on a GLRT approach, using ATI SAR data and considering two different models for the target signal: deterministic and Gaussian model. We will show also some preliminary results on real TerraSAR-X data.

In this paper we investigate the problem of detecting single coherent scatterers in multidimensional (elevation-velocity) SAR imaging. We exploit both phase and amplitude information and a GLRT approach. Multipass/multiview SAR data are used and performances are assessed referring to two different models for the scatterers: deterministic and Gaussian model.

Phase unwrapping (PU) is one of the key processing step in reconstructing the digital elevation model (DEM) of a scene from interferometric synthetic aperture radar (InSAR) data. The PU problem entails the estimation of an absolute phase from the observation of its noisy principal (wrapped) values. This paper presents a 2D InSAR phase unwrapping algorithm exploiting the capability of the Kalman algorithm to simultaneously perform noise filtering and phase unwrapping. The performance of the proposed algorithm is tested on simulated InSAR images of the Gaussian type proving the effectiveness of our method.

Along-track interferometric synthetic aperture radar systems can be used for ground moving target indication. We analyze a scheme for detecting moving targets with unknown parameters (velocity and signal-to-clutter ratio) and with constant false-alarm rates, based on the generalized likelihood ratio test (GLRT), and adopting a Gaussian model for target and clutter signals. We compare its performance with the one obtained in the ideal case of known target parameters applying the likelihood ratio test (LRT). A closed form for the LRT receiver operating characteristic is derived and used as reference for GLRT performance assessment. The analysis is carried out on simulated TerraSAR-X data.

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.

Recently, a 3-D SAR imaging technique based on Compressive Sampling (CS) has been introduced . It allows to noticeably decrease the number of acquisitions required and to obtained an increased elevation resolution. The number of acquisitions required to obtain a reliable 3-D reconstruction is related to the desired resolution and to the overall elevation extension of the scene. If the number of measurements is not sufficiently high, artifacts can appear in the reconstruction. In this paper, this issue is investigated on COSMO-Skymed simulated data.

Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.

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.

In this paper a 3-D SAR imaging technique based on Compressive Sampling is experimented on ERS 1-2 data. The technique is based on the sparsity property of the image to be focused along the elevation direction (i.e. only few scatterers with different elevation are present in the same range-azimuth resolution cell), exploits a reduced number of unevenly spaced acquisitions and allows an increased elevation resolution. Numerical results on real data are compared with those obtained by using Truncated Singular Value Decomposition (TSVD) techniques.

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.

Three dimensional (3-D) Synthetic Aperture Radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range and elevation co-ordinates. For 3-D image focusing multiple signals, acquired along different orbits, are required. The practical application of the focusing methods requires that non-uniformly spaced acquisition orbits have to be considered. In this paper we propose a technique exploiting the Compressive Sampling theory, and assuming that the image to be focused has a sparse representation along the elevation directions, which amounts to suppose that only few point-like scatterers with different elevation are present in the same range-azimuth resolution cell. Numerical results on simulated data show the good performance of the method.

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.

Multi-baseline interferometric Synthetic Aperture Radar (In-SAR) systems are used to obtain increased accuracy Digital Elevation Model (DEM) of the observed ground scene. The techniques which are commonly used exploit only the interferometric phase information, and are based on Maximum Likelihood (ML) estimation. Due to the difficulty of express the multi-baseline likelihood function in closed form, they adopt the statistical independence approximation of the interferometric phases. In this paper we present a method exploiting both amplitude and phase of the interferometric images, and allowing to express the multi-baseline likelihood function in closed form. It has also the advantage of correctly performing multi-baseline speckle reduction on the image intensity.

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).

Multiple SAR signals acquired along different orbits can be exploited for reconstructing a three dimensional (3-D) reflectivity profile of the scene along azimuth, range and elevation co-ordinates. For the 3-D image formation, the problem of the non-uniform spacing of the orbits has to be considered. In this paper we propose a technique based on two steps: 1) a preprocessing step, in which the samples of the multi-pass signal is computed on a grid which is uniform in the elevation direction, starting from its unevenly spaced samples; 2) a 3-D image focusing based on a simple 3-D convolution operator. The technique proposed has the main advantages of preserving numerical efficiency and allowing to easily include information on the signals bandwidth in the pre-processing step, in such a way to regularize the problem and obtain stable solutions.

In this paper, two techniques for estimating accurate height profiles of the ground, using multi-baselines interferometric synthetic aperture radar (In-SAR) data and an a-priori inaccurate digital elevation model (DEM) of the observed scene, are analyzed. The methods are both based on maximum likelihood (ML) estimation: the first estimates directly the quota of each pixel of the image, independently from the other pixels, while the latter estimates the parameters of the local planes which best approximate, in the ML sense, the height profile in a small neighborhood of each pixel. The inclusion of this contextual information allows improving the estimation accuracy. Results on simulated and real ENVISAT-ASAR data are presented.

We propose a new feature selection algorithm for remote sensing image classification. Our approach has been especially devised for applications in which there is a large number of different features that can be potentially selected, implying that the search space is complex and high-dimensional. In this framework, our proposal is that of reformulating the feature selection problem as the search for the optimal subspace in which the different classes are more effectively discriminated. The search has been performed by using a genetic algorithm in which each individual encode the choice of a subspace, and its fitness is a measure of the class seperability in that subspace. The experimental results, performed on two databases, confirmed the effectiveness of the approach.

Segmentation algorithms are often used in many image processing applications like compression, restoration, content extraction, and classification. In particular as for content extraction works carried out in the past decade have demonstrated that multi-frequency fully polarimetric SAR observations are particularly interesting, thanks to physical properties of the backscattered signal at various frequencies and polarizations. To achieve a good classification, the main difficulty is that SAR images are often embedded in heavy speckle. Segmentation of multi/hyperspectral (optical) imagery is obtained by means of algorithms based on image models, which exploit the spatial dependencies of land-covers. Unfortunately, speckle noise hides such spatial dependencies in observed SAR data. With the aim of investigating on a content extraction algorithm capable of discriminating cover classes present in the observed SAR image, heterogeneity features are used here to emphasize spatial dependencies in the data. Thus, observed pixel values are mapped into features, that take "similar" values on "similar" textures. This allows for using the same procedure of the optical case. Obviously, homogeneity/heterogeneity feature and segmentation quality are fundamental for classification accuracy. Here, the problem is tackled through the joint use of information theoretic SAR features and of a segmentation algorithm based on Markov Random Fields (MRFs).

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.

Segmentation algorithms are often used in many image processing applications like compression, restoration, content extraction, and classification. In particular as for the content extraction, works carried out in the past decade have demonstrated that multi-frequency fully polarimetric SAR observations content are particularly interesting, thanks to physical properties of the backscattered signal at various frequencies and polarizations. To achieve a good classification, the main difficulty is that SAR images are often embedded in heavy speckle. Segmentation of multi/hyperspectral (optical) imagery is obtained by means of algorithms based on image models, which exploit the spatial dependencies of landcovers. Unfortunately, speckle noise hides such spatial dependencies in observed SAR data. With the aim of investigating on a content extraction algorithm capable of discriminating cover classes present in the observed SAR image, homogeneity/heterogeneity features are used here to emphasize spatial dependencies in the data. Thus, observed pixel values are mapped into features, that take "similar" values on "similar" textures. This allows for using the same procedure of the optical case. Obviously, homogeneity/heterogeneity feature and segmentation quality are fundamental for classification accuracy. Here, the problem is tackled through the joint use of information-theoretic SAR features and of a segmentation algorithm based on Markov Random Fields (MRFs).

In this paper the application of a transform coding technique, based on overcomplete independent component analysis (ICA), for the compression of single look intensity synthetic aperture radar (SAR) images is explored. The method has the advantage of representing the image through almost statistically independent coefficients, with an assigned distribution, so that a scalar entropy constrained quantizer, optimized for the coefficients statistics, can be used. Numerical results on ERS-1 data are presented.

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 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.

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.

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.

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.

An SAR simulator of an extended three-dimensional scene is presented. It is based on a facet model for the scene, asymptotic evaluation of SAR unit response, and a two-dimensional fast Fourier transform code for the data processing. Prescribed statistics of the model account for a realistic speckle of the image. The simulator is implemented in Synthetic Aperture Radar Advance Simulators (SARAS), whose performance is described and illustrated by a number of examples.>