Multibaseline and tomographic synthetic aperture radar (SAR) data are often affected by phase distortions known as phase screens. These distortions stem either from atmospheric effects or residual errors in platform motion. Calibrating and compensating for the phase screen is crucial to prevent spreading and defocusing in multidimensional tomographic imaging. Given the growing interest in artificial intelligence and deep learning, we aim to utilize their potential to develop a phase calibration process for SAR tomographic data. Our proposed framework is based upon a convolutional neural network (CNN) and generates training patches directly from the tomographic images under consideration, without relying on external references or resources. Once trained, the network effectively estimates phase distortions across the entire image; these are then used to calibrate the tomographic data. Experimental results from AfriSAR and UAVSAR tomographic datasets are included to showcase the effectiveness of the proposed solution.

In the above article [1], the correct reference associated with equation (2) on page 3, left column, is the below paper: 1) P. Imperatore and G. Fornaro, “Joint Phase-Screen Estimation in Airborne Multibaseline SAR Tomography Data Processing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1–14, 2024, Art. no. 4412614, doi: 10.1109/TGRS.2024.3446186.

Polarimetric Synthetic Aperture Radar (PolSAR) images offer a rich repository of information, crucial for diverse applications ranging from classification to target identification. In the domain of PolSAR image classification, the Wishart classifier emerged as a prominent and widely employed technique. This classifier is often used to articulate the properties of polarimetric scattering types in images, providing valuable insight into various types of targets. With the growing interest in multidisciplinary Artificial Intelligence (AI) research, especially in computer vision and Natural Language Processing (NLP), our goal is to integrate this enthusiasm into polarimetric image analysis. We propose extending the Wishart classifier framework to include a free-form and open-ended Visual Question Answering (VQA) model. This model is designed to answer natural language questions related to PolSAR images, covering pixel details and scattering patterns. The objective is to provide accurate natural language responses that reflect real-world scenarios, such as assisting the visually impaired. Both questions and answers in this context are intentionally left open-ended to capture the complexity of inquiries in the polarimetric SAR images domain.

This paper addresses the phase miscalibration within the broader scope of tomographic synthetic aperture radar (SAR) image focusing. Phase errors, typically independent from one acquisition to another, result in a dispersion and defocusing effect in the multi-dimensional imaging space. Dealing with this issue, particularly in the presence of volumetric scattering, poses a significant challenge. In this paper, we propose a novel framework for phase calibration in tomographic data. Our proposed framework employs deep learning solutions to estimate calibration phases, eliminating the need for model assumptions, external data sources, or phase unwrapping. In this approach, training data patch are directly derived from the data under consideration. Post-training, the network can effectively mitigate atmospheric phase screens or calibrate individual interferograms. Experimental results are included to demonstrate the effectiveness of the proposed method.

Reducing speckle while preserving complex structures in images has always been a significant challenge in processing of Synthetic Aperture Radar (SAR) images. This paper proposes a new despeckling method for dual-polarimetric SAR images based on the conditional diffusion model. By explicitly learning specific distributions from the training data, this method better restores the image structures. To support this research, a VV-VH dual-polarimetric dataset is constructed using multitemporal fusion techniques with data obtained from the Sentinel-1 satellite. The proposed method is compared with five other SAR despeckling methods. The results show that this method performs better in preserving image details and effectively removing speckle. Furthermore, this paper introduces a new sampling method for SAR despeckling. Compared to the two existing methods, it achieves better despeckling results and superior structural preservation.

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.

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.

Satellite images of synthetic aperture radar (SAR) sensors are contaminated by speckles from the coherent imaging mechanism. Although removing or mitigating speckle has been a critical issue for SAR applications, effective reduction continues to be a significant challenge for existing methods when preserving the intricate structures within SAR images. To address this issue, this work proposes a novel conditional diffusion model for SAR despeckling (DiffusionSAR). The new method explicitly learns data distributions by forward diffusion toward multiplicative gamma noise. The logarithmic and Yeo–Johnson (log-Yeo–Johnson) transformation are harnessed in preprocessing for fine-tuning or hybrid training. A prolonging steps technique is suggested in fine-tuning to match the preprocessing. A new synthetic dataset is designed for satellite SAR despeckling. The proposed method is compared with eight state-of-the-art methods using both synthetic and real-world SAR satellite images. The qualitative and quantitative evaluations confirm the effectiveness of the proposed method in structural preservation as well as noise reduction. A fine-tuning experiment using stacked multitemporal data shows the necessity of tine-tuning training in bridging the domain gap when trained with synthetic data and tested with real-world SAR data.

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images, compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

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) systems can be designed with different polarimetric modalities. Most spaceborne SAR systems acquire dual-polarimetric data to meet various operational requirements. They are designed to capture more information about the Earth’s surface than single-pol systems and to cover a wider area than full-pol modalities. Dual-polarimetric data may not be as informative as fully polarimetric images. Several methods exist to augment dual-polarimetric images to take the capabilities of fully polarimetric data. Such methods, nevertheless, are either specific to special dual-pol modalities, i.e., compact modes, or rely on model assumptions that may not be valid in various scattering scenarios. In this article, a new framework for reconstructing fully polarimetric information from typical modalities of dual-pol data is proposed. The framework uses deep learning solutions to augment dual-polarimetric data without relying on model assumptions. Besides the specific architecture of the network used, which makes it efficient to extract distinctive features, a specific loss function is defined to account for the different scattering properties of the polarimetric data. Experiments on different real data show that the reconstruction performance of the proposed framework is superior to the conventional reconstruction method that widely experimented in the literature. Moreover, the pseudo-fully polarimetric data reconstructed by the proposed method closely match the actual fully polarimetric images acquired by radar systems, confirming the reliability and effectiveness of the proposed method.

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.

PolSAR (Fully Polarimetric Synthetic Aperture Radar) imagery is used in Earth observation and remote sensing for various applications such as land use and land cover classification, change detection, etc. The occurrence of speckle in PolSAR images, however, degrades the performance of all image processing techniques and therefore prevents their full use for various applications. Several speckle reduction methods have been developed in the literature over the last forty years, highlighting the importance of this issue. Despite this extensive knowledge, speckle removal is yet an open problem that is far from being fully solved. Recently, Deep Learning (DL) has achieved great success in speckle reduction of SAR images. The data-driven nature of this technique provides improved flexibility and the ability to capture a variety of features from PolSAR images, thereby enhancing the performance of the speckle reduction process. In this paper, a new despeckling technique is proposed in the context of deep convolutional neural networks for de-noising the polarimetric covariance or coherence matrix. The method controls the training process with respect to several characteristic features of PolSAR images defined by the combination of three different cost functions. In particular, the goal is to balance the different features, including spatial details and speckle statistical properties, in the denoising process. The proposed method is experimentally validated with real airborne datasets and compared with existing despeckling approaches.

Recently, the synthetic aperture radar tomography (TomoSAR) technique has attracted significant attention owing to its 3-D reconstruction capability of complex urban environments. The availability of a high number of images is usually a requirement for nonparametric spectral estimation methods. This letter evaluates the potential of four nonparametric spectral estimation algorithms, that is: 1) linear prediction (LP); 2) minimum norm (MN); 3) singular value decomposition (SVD); and 4) Capon for improved tomographic reconstruction of the third dimension of built-up areas with a small number of observations. The performance analysis is carried out for both simulated and real SAR datasets. The returns from the employed techniques indicate the efficient and low-computational estimator of LP by minimizing the average output signal power at the array of antenna elements and make it possible to separate multiple scatters at a distance below the Rayleigh resolution and clean sidelobes’ phenomena in the elevation profiles. The experimental results of a dataset acquired by the TerraSAR-X sensor verify the effectiveness of the LP spectral estimator algorithm in the reconstruction of urban buildings. The estimated height of scatterers with the LP method is considerably similar to the ground-observed data.

Synthetic aperture radar (SAR) interferometry (InSAR) has shown great potential in the monitoring of Earth’s surface and detection of the possible slow temporal deformations. Within the framework of multibaseline SAR interferometry, the availability of multiple interferograms obtained from multipass satellite observations can significantly improve the accuracy of the estimated target parameters, i.e., the residual height and the mean deformation velocity. The parameters can be estimated in the maximum likelihood (ML) sense and through the data covariance matrix. However, the presence of artifact and outliers may impair the parameter estimation, specifically when the candidate cells are subject to temporal decorrelation and atmospheric phase noise effects. In this letter, the exploitation of contextual spatial information is proposed to reduce the possible ambiguity and improve the accuracy of ML-based parameter estimation. The proposed approach adds a regularization term (or a constraint) to the ML’s model in order to include the information about the scene velocity variation. Hence, the resulted nonconvex optimization is resolved using the graph-cut concept. The method is evaluated using the simulated and two real data sets acquired by Constellation of Small Satellites for Mediterranean basin Observation (COSMO-SkyMed) and Sentinel-1A sensors over Tehran, Iran; and the results are validated using the global positioning system-based measurements.

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.

This paper addresses the 3D classification of superimposed target scattering mechanisms in polarimetric synthetic aperture radar (SAR) images of urban environments. Theoretically, the solution of this problem is possible with polarimetric tomographic SAR focusing techniques. In this work, we investigate how SAR Tomography (TomoSAR) can be used to identify and distinguish the superimposed target mechanisms. In particular, various strategies are employed to separate and characterize the polarimetric scattering patterns of targets in layover regions. Extensive and comparative analyses are performed to answer how accurately different strategies can identify the polarimetric scattering pattern.

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 available methods for geodetic measurements, synthetic aperture radar (SAR) interferometry (InSAR) has been considered as a powerful tool for the monitoring of earth's surface, digital elevation model generation and possible slow temporal deformation mapping. In this context, multi-baseline SAR interferometry with the availability of multiple interferograms obtained from multi-pass satellite observations significantly improves the accuracy of the estimated target's parameters, i.e. the residual height and the mean deformation velocity. In this paper contextual spatial information has been exploited as a regularization term in order to improve the capability of multibaseline SAR Interferometry in dealing with possible artifacts and outliers induced by temporal decorrelation and remained atmospheric phase noise effects which can impair the accuracy of estimated target's parameters. The superiority of regularized processing is related to depletion of velocity variations over the scene and reducing ambiguity in parameter estimation. The proposed method is evaluated using a simulated and a real data set acquired by COSMO-SkyMed sensor over Tehran, Iran; and the results are compared with conventional adapted approach in the literature. The evaluation indicated that adaptation of contextual information can significantly improve the interferometric-based parameter estimation over partially coherent targets which are affected by outliers and artifacts.

With reference to the application of multichannel (polarimetric or interferometric) synthetic aperture radar (SAR) data, despeckling is a mandatory task in order to exploit fully the image information. Thanks to the long-standing studies, there exist several despeckling techniques that have shown to be powerful tools for filtering multichannel SAR images. Often, such techniques rely on the estimation of the covariance matrix. However, in the literature, the performance evaluation of the filtering operation, which represents a fundamental aspect, has been mainly addressed for a single intensity/amplitude SAR image. In this letter, we present two nonreference indices for the evaluation of the polarimetric/interferometric covariance matrix. The proposed quality indices assess the statistical similarity of the ratio between the original and filtered covariance matrices to the properties derived from the pure speckle model. The analyses both on simulated and real data show that the results of filter ranking by the proposed method are in agreement with the visual evaluation and are consistent with the common polarimetric quality measures.

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.