SEGMENTATION OF SYNTHETIC APERTURE RADAR COMPLEX DATA

In this paper, we present an integrated, end-to-end synthetic aperture radar (SAR) processing system that accepts data processing requests, submits processing jobs, performs quality analysis, delivers and archives processed data. This fully automated SAR processing system utilizes database and internet/intranet web technologies to allow external users to browse and submit data processing requests and receive processed data. It is a cost-effective way to manage a robust SAR processing and archival system. The integration of these functions has reduced operator errors and increased processor throughput dramatically.

A comprehensive and effective parallel airborne synthetic aperture radar (SAR) processing system using the architecture of central processing units (CPU) and graphical processing units (GPU) is proposed first in this paper. By using the techniques of Compute Unified Device Architecture, the SAR processing system is much more efficient and robust, thereby enabling it to work with high efficiency. This paper focuses on the optimizations of motion compensation, subaperture chirp scaling algorithm, phase gradient autofocus (PGA), and even visualization with Open Source Computer Vision Library. Apart from this, it is also the first time the PGA has been run on the architecture of CPU and GPU. Experimental results show a speedup of ∼37 times compared with a nonoptimized CPU-based approach.

Coherent processing of synthetic aperture radar (SAR) data makes SAR images highly susceptible to signal‐dependent granular noise known as speckle. For accurate texture estimation, the maximum a posteriori (MAP) approach is found to be efficient among multitude of despeckling approaches reported in the available literature. However, the formulations of MAP estimator for texture are available in ‘intensity format’. The authors formulate ‐MAP and ‐MAP estimators for texture in ‘amplitude format’, considering clutter to be and distributed. The parameters associated with and distributions are estimated using Mellin transform and second‐kind statistics. Furthermore, an MAP‐based filter is proposed here for mixture of Cauchy–Exponential compound Gaussian (CE‐CG) distributed clutter data where the texture is modelled using CE mixture model. For estimation of CE model parameters, they employ expectation–maximisation (EM) algorithm. Finally, the ‐MAP, ‐MAP, and CE‐MAP estimation of texture both in ‘amplitude format’ and ‘intensity format’ are performed on single look real SAR clutter data and also on multilook synthetic data. Experimental results illustrate the superiority of CE‐MAP filter and moreover that the MAP estimation in ‘amplitude format’ achieves better mean preservation and speckle suppression compared with the same in ‘intensity format’.

The success of target reconstruction in the SAR(Synthetic Aperture Radar) imaging system is greatly dependent on coherent detection. Incoherent detection appears as a multiplicative phase error to echoed signal, which consequently causes fatal degradations such as fading or dislocation of target image. In this paper, we propose a motion error correction scheme using an in-scene target to compensate for relative distance error between the radar and the target. We start by modeling from a wave equation for one point target, and then derive the complex motion error from extended overall echoed data. The proposed algorithm is also good for the correction of relatively large motion error because it can be applied repeatedly, and it is converged after each iteration. We use the spatial Doppler characteristics of the strong in-scene target to retrieve motion error, thus, we only used the partial spectral echo data corresponding to the strong in-scene target. By doing this, we can reduce computational loads and the number of iterations for the large motion error. We verify the performance of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

Synthetic Aperture Radar (SAR) echo simulation is an effective tool of the radar system design, which enables us to validate imaging algorithms and evaluate the performance of SAR systems before actual use. The increasing demand of higher SAR resolution makes the SAR simulation more and more complicated. The traditional simulation methods are not precise enough so that a new simulation method applicable to most complicated targets is needed. This paper demonstrates a novel simulation method based on the theory of electromagnetic scattering which simulates the received echo data through taking use of the backscattered electric field. Our simulation method can be used to simulate a complicated target in SAR simulation. This method opens a door to conducting SAR echo simulation with theory of signal and system and the electromagnetic scattering taken into consideration, though a few improvements are needed to increase the performance of the SAR simulation.

The paper provides a review into the steps involved in acquiring and processing synthetic aperture radar (SAR) data transmitted by the European remote sensing (ERS) satellites. The paper reports on a simple MATLAB-based SAR processing system, that reads the image out of the complex SAR data files and that is suitable for use in the classroom to demonstrate one of the procedures used in SAR data processing. The paper can also help a beginner in the field of SAR signal processing to get information and understand the basics that are necessary to acquire and process a SAR image. The data, provided by the Alaskan Satellite Facility (ASF), is categorized into different levels and the paper describes the process of obtaining the level-1 basic image from the level-0 raw data file provided by ASF.

The authors tested the applicability of coherence information for optimizing SAR (synthetic aperture radar) interferogram. The purpose of the optimization in this study is to improve the smoothness of the topographic fringe in a SAR interferogram, which results in better fringe for unwrapping. Coherence information was computed as the complex correlation coefficient between two SAR complex data. To optimize the interferogram, the maximum coherence value was searched in 3/spl times/5 window around the initial registered position between two data and then the phase difference was obtained from the pixel pair that has the maximum coherence. This optimizing method was attempted for several SAR complex data pairs. The effect of the optimization was found in some pairs. It was found that the increase of the coherence clue to the optimization was one of the important factors for the improvement of the smoothness.

Radio frequency electromagnetic interference is a relatively common phenomenon, especially for synthetic aperture radar (SAR) systems working in P- or L-band. Compared with the suppression of narrowband interference, that of wideband interference, particularly of those whose signal parameters have frequently changing property, is still a sophisticated problem. In this letter, a suppression algorithm for interference with wideband and complicated parameters is proposed, based on low-rank and sparse matrix decomposition (LRSMD) in time–frequency domain (TFD) of the signal. The proposed algorithm begins with transforming the SAR signal into TFD. After that, LRSMD based on bilateral random projection (BRP) is applied to decompose the time–frequency spectrum matrix into three parts, a low-rank matrix standing for interference, a sparse matrix standing for SAR signal, and a noise matrix. Finally, inversely transform the sparse matrix into the time domain to obtain SAR signal without interference. The proposed algorithm is applied to a single look complex (SLC) SAR data of Sentinel-1 to validate its effect and efficiency.

Synthetic Aperture Radar (SAR) imaging systems are nowadays very common technics of imaging in remote sensing and environment survey. There are different acquisition modes: spotlight, stripmap, scan; different geometries: mono-, bi- and multi-static; and varieties of specific applications: interferometric SAR (InSAR), polarimetric SAR etc. In this paper, first a common inverse problem framework for all of them is given, and then basics of SAR imaging and the classical deterministic inversion methods are presented. Aiming at overcoming the inadequacies of deterministic methods, a general probabilistic Bayesian estimation method is pioneered for solving image reconstruction problems. In particular, two priors which simply allow the automated determination of the hyperparameters in a Type-II likelihood framework are considered. Finally, the performances of the proposed methods on synthetic data.

Synthetic aperture radar (SAR) provides high-quality images in all weather and all time conditions. It shows promise for acquiring spatial and temporal data for urban analysis. The objective of this study is threefold. First, the mathematical theory and physical background of SAR data acquisition and processing are described and introduced to the civil and engineering mechanics community. Second, a simulation tool based on the electromagnetic formulation is used to simulate SAR imagery of real-sized buildings as a proof of concept to demonstrate the usefulness of SAR applications and their imaging potentials and characteristics relevant to urban areas. Our third and last objective is to study the capabilities of damage and change diagnostic schemes based on simulated complex SAR images of pre- and post-events. A SAR image is obtained from a coherent imagery system and can be described in three dimensions, sometimes referred to as a complex imagery system, whereby two dimensions represent the spatial event of the image and the third dimension represents the phase content of the image. The phase information is relevant to the detection of the finer details in the image.

This letter presents a precise and rapid clustering method for synthetic aperture radar (SAR) images by embedding a Markov random field (MRF) model in the clustering space and using graph cuts (GCs) to search the optimal clusters for the data. The proposed method is optimal in the sense of maximum a posteriori (MAP). It automatically works in a two-loop way: an outer loop and an inner loop. The outer loop determines the cluster number using a pseudolikelihood information criterion based on MRF modeling, and the inner loop is designed in a ldquohardrdquo membership expectation-maximization (EM) style: in the E step, with fixed parameters, the optimal data clusters are rapidly searched under the criterion of MAP by the GC; and in the M step, the parameters are estimated using current data clusters as ldquohardrdquo membership obtained in the E step. The two steps are iterated until the inner loop converges. Experiments on both simulated and real SAR images test the performance of the algorithm.

Recently, Convolutional Neural Network (CNN) has achieved some success in synthetic aperture radar (SAR) image classification. This outstanding performance mainly depends on a large number of training samples, but achieved satisfactory classification results with limited training samples remains a challenge. To address this problem, we propose a dual-scale Siamese densely connected network with Markov Random Fields (DS-SDCNet-MRF) for single-polarization SAR image classification. First, the Siamese densely connected network (SDCNet) is proposed to fully extract discriminative features under limited samples. Then, the proposed DS-SDCNet is constructed with two SDCNets of different scales to produce complementary classification results. Among them, the large-scale SDCNet has a better classification result in the homogeneous region, while the small-scale SDCNet tends to provide good detail preservation. Finally, an improved MRF model which combined the category probability information is proposed to further improve the classification performance. Experimental results on simulated and real single-polarization SAR data demonstrate that the proposed method achieves more encouraging classification performance than the current state-of-the-art classification methods with limited samples.

Detailed information on spatial distribution of wetland classes is crucial for monitoring this important productive ecosystem using advanced remote sensing tools and data. Although the potential of full- and dual-polarimetric (FP and DP) Synthetic Aperture Radar (SAR) data for wetland classification has been well examined, the capability of compact polarimetric (CP) SAR data has not yet been thoroughly investigated. This is of great significance, since the upcoming RADARSAT Constellation Mission (RCM), which will soon be the main source of SAR observations in Canada, will have CP mode as one of its main SAR configurations. This also highlights the necessity to fully exploit such important Earth Observation (EO) data by examining the similarities and dissimilarities between FP and CP SAR data for wetland mapping. Accordingly, this study examines and compares the discrimination capability of extracted features from FP and simulated CP SAR data between pairs of wetland classes. In particular, 13 FP and 22 simulated CP SAR features are extracted from RADARSAT-2 data to determine their discrimination capabilities both qualitatively and quantitatively in three wetland sites, located in Newfoundland and Labrador, Canada. Seven of 13 FP and 15 of 22 CP SAR features are found to be the most discriminant, as they indicate an excellent separability for at least one pair of wetland classes. The overall accuracies of 87.89%, 80.67%, and 84.07% are achieved using the CP SAR data for the three wetland sites (Avalon, Deer Lake, and Gros Morne, respectively) in this study. Although these accuracies are lower than those of FP SAR data, they confirm the potential of CP SAR data for wetland mapping as accuracies exceed 80% in all three sites. The CP SAR data collected by RCM will significantly contribute to the efforts ongoing of conservation strategies for wetlands and monitoring changes, especially on large scales, as they have both wider swath coverage and improved temporal resolution compared to those of RADARSAT-2.

Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification algorithms have made remarkable achievements. But the acquisition and annotation of SAR target images are time-consuming and laborious, and it is difficult to obtain sufficient training data in many cases. The insufficient training data can make deep learning based models suffering from over-fitting, which will severely limit their wide application in SAR target classification. Motivated by the above problem, this paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset. To overcome the sample restriction problem caused by the poor feature discriminability for real SAR data. A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the feature discriminability. Based on the proposed regularization method, we design a transfer-learning pipeline to leverage the simulated SAR data as well as acquire better feature discriminability. The experimental results indicate that the proposed method is feasible for the sample restriction problem in SAR target classification. Furthermore, the proposed method can improve the classification accuracy when relatively sufficient training data is available, and it can be plugged into any convolutional neural network (CNN) based SAR classification models.

In a previous study [1], the joint use of Synthetic Aperture Radar (SAR) image magnitude and phase has been assessed in the context of target detection. The two-looks Internal Hermitian Product (2L-IHP), based on a correlation between sub-looks of a single look complex (slc) radar image, was introduced. It has been applied on an airborne radar image including deterministic targets deployed in a natural environment. The 2L-IHP was shown to increase the target environment contrast. The purpose of the current work is to capture the 2L-IHP behavior through the simulation aspect. For this, a SAR processor has been developed, and applied to simulated targets with known response. The objectives of this study are to capture the behavior of point targets (be they coherent during all the synthesis or partly), and speckle when using the 2L-IHP. The impact of position and width of the sub-looks band will also be addressed. Then the detection efficiency of the 2L-IHP will be compared to other target detection techniques.