Synthetic Aperture Radar Radar Research Articles

Sea State Characterization Using Experimental Synthetic Aperture Radar Raw Data in Two-Dimensions and the Modified Fractal Signature Method

This paper presents a novel method for the characterization of the sea state using a set of raw experimental Synthetic Aperture Radar (SAR) data in two dimensions, i.e., in the “fast time” and “slow time” directions (as explained in the text) and the so-called “Modified Fractal Signature” (MFS) method. That is, experimental SAR radar signatures in the above two dimensions (i.e., “raw data” in the time domain) were provided to our research group by the Norwegian Institute of Defence (FFI Institute), Norway, which we processed and analyzed using the MFS method in a novel way, as presented in detail in this paper. The numerical results obtained here show an easy categorization of the sea surface as “calm sea” or “turbulent sea”, thus establishing a very promising technique for the characterization of sea state in real time, as described in detail in the text.

Reference Map Generation for Synthetic Aperture Radar Based on CycleGAN

The real-time closed-loop simulator of Synthetic Aperture Radar (SAR) needs to input the SAR radar reference map as the simulation target scene information in the process of simulation. However, it is difficult to obtain the scene map of SAR radar in the detection range that is realistic and consistent with the task target, and the data sets are limited. In this article, Cycle-Consistent Adversarial Networks (CycleGAN) is applied to realize the migration learning of SAR radar reference map and solve the task required of a directional generation of SAR images.

All-Sky Soil Moisture Estimation over Agriculture Areas from the Full Polarimetric SAR GF-3 Data

This study aims to estimate the soil moisture (SM) in all-sky agriculture areas using fully polarimetric synthetic aperture radar (SAR) Gaofen-3 (GF-3) data. The radar vegetation index (RVI) is obtained using the radar SAR data, which overcomes the difficulty that the optical data cannot construct the vegetation index in cloud-covered areas. The RVI is introduced into the water cloud model (WCM) to remove the contribution of vegetation to the total radar backscatter σ0 and obtain the soil backscattering coefficients σsoil0 with HH and VV polarization. Subsequently, σsoil0 and radar frequency data are introduced into the Chen model, and a semi-empirical model of SM estimation is established. The main findings of this study are as follows: (1) Compared with the σ0, the σsoil0 obtained by the WCM has a stronger correlation with the SM. (2) In the cloud covered area, the accuracy of the estimated SM by synergistically using the WCM and the Chen model is ideal. An RMSE of 0.05 and a correlation coefficient (r) of 0.69 are achieved. In this study, the SM estimation method is not affected by clouds, and it shows many advantages for sustainable development, monitoring soil drought degree, and other related research.

Sea State Characterization Using Experimental One-Dimensional Radar Signatures and Fractal Techniques

This paper presents a novel method of sea state characterization by using four criteria, which are applied to normalized experimental Synthetic Aperture Radar (SAR) one–dimensional signatures (range profiles), provided to our research group by SET 215 Working Group on “SAR radar techniques”. These criteria are the “Fractal Dimension”, “Fractal Length”, “Variance σ2”, and “Power Spectrum Density - Least Squares”. The “Fractal Dimension” and “Fractal Length” criteria, which appear to be the most important out of the four criteria, use the “blanket” technique to provide sea state characterization from SAR radar range profiles. It is based on the calculation of the area of a “blanket”, corresponding to the range profile under examination, and then on the calculation of the corresponding “Fractal Dimension” and “Fractal Length” of the range profile. The main idea concerning this proposed technique is the fact that normalized SAR radar range profiles, corresponding to different sea states, produce different values of “Fractal Dimension” and “Fractal Length” for all angles of incidence examined here. As a result, a sea state characterization technique for two different sea states (turbulent and calm sea) is presented in this paper.

Efficient Contourlet Transformation Technique for Despeckling of Polarimetric Synthetic Aperture Radar Image

In Polarimetric SAR (PolSAR) and synthetic aperture radar (SAR) requires efficient filtering approach for effective processing of captured quad-polarized images. Due to polarization speckles arises in the captured PolSAR images which causes complexity in collection of data from PolSAR images. This limitation has been overcome through application of effective despeckling technique for efficient processing of PolSAR images. Several techniques have been evolved over past three decades to reduce those speckle in image captured by PolSAR, and recent research studies illustrated a efficient trend for filtering local single-point to non-local patch-based or global collaborative filtering. In this paper, applied contourlet technique for effective despeckling of PolSAR images. This contourlet transformation technique perform thresholding and transformation for efficient despeckling of captured images. Contourlet transformation technique is applied in PolSAR images with speckle and performance metrices are observed. Contourlet technique is comparatively examined with conventional transformation technique which involved in despeckling of images. Analysis of results exhibited that DWT (Discrete Wavelet Transform) approach exhibits similar performance related to contourlet transformation technique. Through analysis of results it is observed that computation time also significantly reduced in contourlet transformation rather than conventional technique. Further mean and window sizing also significantly maintained in contourlet technique for various noise variance.

Real time FPGA implemnation of SAR radar reconstruction system based on adaptive OMP compressive sensing

<p><span style="font-size: 9pt; font-family: 'Times New Roman', serif;">Synthetic Aperture Radar (SAR) is an imaging system based on the processing of radar echoes. The produced images have a huge amount of data which will be stored onboard or transmitted as a digital signal to the ground station via downlink to be processed. Therefore, some methods of compression on the raw images provides an attractive option for SAR systems design. One of these techniques which used for image reconstruction is the Orthogonal Matching Pursuit (OMP). OMP is an iterative algorithm which need high computational operations. The computational complexity of the iterative algorithms is high due to updating operations of the measurement vector and large number of iterations that are used to reconstruct the images successfully. This paper presents a new adaptive OMP algorithm to overcome this issue by using certain threshold. The new adaptive OMP algorithm is compared with the classical OMP algorithm using the Receiver Operating Characteristic (ROC) curves. The MATLAB simulations show that the new adaptive OMP algorithm improves the probability of detection at lower SNRs, reduce the computational operations as well as the number of required iterations. FPGA implementation of both the classical OMP and the adaptive OMP algorithm are also presented in this paper.</span></p>

Seismic damage assessment in Sarpole-Zahab town (Iran) using synthetic aperture radar (SAR) images and texture analysis

The synthetic aperture radar SAR system with the capability of imaging during the night, day, and the all-weather conditions has a high potential in change detection on the ground surface. In this research, we used three SAR images of ALOS-2 satellite over Sarpole-Zahab town in the west of Iran that had an earthquake with the magnitude of 7.3 on November 12, 2017. The effects of speckle noise on the accuracy of the results were assessed based on noise reduction filters. Correlation coefficient, difference of intensity (in five window sizes), and difference of coherence and texture (in six window sizes) of the pre- and post-event images were calculated, and the output parameters were extracted. Then, the damage assessment was carried out based on four machine learning classifiers, containing the random forest (RDF), the support vector machine, the naive Bayes classifier, and K-nearest neighbor. The RDF showed an overall accuracy of 86.3%. Seventy percent of the dataset was used for training, and 30% of it was used for the prediction purpose (~ 300 buildings). Based on the training dataset, the total number of structures in the study area was predicted (approximately 9200 buildings). Finally, a discriminant analysis was carried out among the damaged and undamaged buildings.

Pipelined Image Reconstruction of SAR Radar Based on Orthogonal Matching Pursuit using FPGA Implementation

Synthetic Aperture Radar (SAR) imaging methods is an interesting field in remote sensing. Nowadays, the Orthogonal Matching Pursuit Compressive Sensing (OMP) algorithm is applied on the reconstruction of data that are produced by SAR. The OMP is iterative algorithm that needs high time consumption and processing delay. This issue is considered one of the main problems that face the designers of the SAR systems that how to speed up the performance of the system to be more applicable. This paper provides an applicable pipelined processing technique for the OMP compressive sensing algorithm to speed up the compression and reconstruction of SAR Image Data. Based on the goal of this paper, it is possible to reduce the time processing of the OMP as every clock a new process will be started and it is not required to wait the certain process to be finished. The good-resolution images of the SAR are used for mapping, identification and other applications. This article defines the compressive sensing algorithms and it also discusses the design, analysis of the proposed pipelined processing method for one of the CS algorithms to reduce the consumed time using FPGA implementation. Moreover, the paper includes the implementation of the both normal processing and the proposed pipelined processing for the used algorithm. Finally, a comparison between the two algorithms is presented to evaluate the performance.

Sea state characterization using fractal techniques on experimental one – dimensional radar signatures

This paper presents a novel method of sea state characterization using the ‘Mean Fractal Length (MFL)’criterion which is applied to experimental Synthetic Aperture Radar (SAR) one – dimensional signatures(range profiles), provided to our research group by SET 215 Working Group on ‘SAR radar techniques’.The MFL criterion uses the ‘blanket’ technique to provide sea state characterization from SAR radarrange profiles. It is based on the calculation of the area of a ‘blanket’, corresponding to the range profileunder examination, and then on the calculation of the corresponding ‘Fractal Length’ of the range profile.The main idea concerning this proposed technique is the fact that SAR radar range profiles correspondingto different sea states yield different values of ‘Fractal Length, FL’, namely ‘turbulent sea’ yields rangeprofiles with larger FL, because of the more ‘anomalous behaviour’ of the range profiles in that case. As aresult, a sea state characterization technique for two different sea states (turbulent and calm sea) ispresented in this paper.

Rapid detection to long ship wake in synthetic aperture radar satellite imagery

The maritime administrative department employs synthetic aperture radar (SAR) satellite remote sensing technology to obtain evidence of illegal discharge of ships. If the ship is discharged during navigation, it forms a long dark wake on the SAR image due to the suppression of the Bragg wave by the oil film. This study investigates key techniques for rapid detection of long ship wakes, thereby providing law enforcement agencies with candidate ships for possible discharge. This paper presents a rapid long ship wake detection method that uses satellite imaging parameters and the axial direction of the ship in images to determine the potential detection area of the wake. Then, the threshold of long ship wake detection is determined using statistical analysis, the area is binarized, and isolated points are removed using a morphological filter operator. The method was tested with ENVISAT Synthetic Aperture Radar and GF-3 SAR data, and results showed that the method was effective, and the overall accuracy of the decision reaches 71%. We present two innovations; one is a method that draws a Doppler shift curve, and uses the SAR imaging parameters to determine the detection area of the long wake to achieve rapid detection and reduce the image detection area. The other is where a classical linear fitting method is used to quickly and accurately determine whether the detected dark area is a long ship wake and realizes the twisted long ship wake detection caused by the sea surface flow field, which is otherwise difficult to detect by the traditional Radon and Hough transform methods. This method has good suppression performance for the dark spot false alarm formed by low speed wind region or upward flow. The method is developed for maritime ship monitoring system and will promote the operational application of maritime ship monitoring system.

MAPPING THE EXTENT AND MAGNITUDE OF SEVER FLOODING INDUCED BY HURRICANE IRMA WITH MULTI-TEMPORAL SENTINEL-1 SAR AND INSAR OBSERVATIONS

Abstract. During Hurricane Irma’s passage over Florida in September 2017, many sections of the state experienced heavy rain and sequent flooding. In order to drain water out of potential flooding zones and assess property damage, it is important to map the extent and magnitude of the flooded areas at various stages of the storm. We use Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) observations, acquired by Sentinel-1 before, during and after the hurricane passage, which enable us to evaluate surface condition during different stages of the hurricane. This study uses multi-temporal images acquired under dry condition before the hurricane to constrain the background backscattering signature. Flooded areas are detected when the backscattering during the hurricane is statistically significantly different from the average dry conditions. The detected changes can be either an increase or decrease of the backscattering, which depends on the scattering characteristics of the surface. In addition, water level change information in Palmdale, South Florida is extracted from an interferogram with the aid of a local water gauge as the reference. The results of our flooding analysis revealed that the majority of the study area in South Florida was flooded during Hurricane Irma.

Aerospace monitoring of the oil pollution of the Caspian Sea on the base of GIS technology and radar space images

In this paper, a review of Synthetic Aperture Radar (SAR)-based techniques, for oil slick at sea observation is proposed. Compared with traditional on-site oil spill monitoring, the use of remote sensing technology has the possibility quickly and accurately find an oil spill area. Currently, for detection the marine oil spill are usually used two types - optical and synthetic aperture radar SAR remote sensing imagery. Among all satellite sensors, SAR is still the most utilized for operational oil spill detection. SAR represents a fundamental tool due to its almost all-weather and all-day imaging capability, providing synoptic maps of the observed scene with a fine spatial resolution and a dense revisit time.

Characterization of Three - Dimensional Rough Fractal Surfaces from Backscattered Radar Data

This paper discusses the scattering of electromagnetic (EM) waves from three – dimensional (3D) rough fractal surfaces using the Kirchhoff approximation. In particular, it introduces a novel method to characterize 3D rough fractal surfaces from spectral information of the backscattered EM wave in Synthetic Aperture Radar (SAR) applications. It represents an important extension of a previous recent paper by the same research group from 2D fractal surfaces to 3D fractal surfaces (the latter representing real – life SAR radar scenes). More specifically, in the present simulation scenarios it is assumed that the radar emits a burst of radar pulses of increasing carrier frequency [therefore a ‘stepped – frequency’ (SF) SAR radar]. By calculating the backscattered EM wave from 3D fractal surfaces as a function of the above radar frequencies (therefore, a ‘spectral method’), it is found here that the slope between the main backscattered lobe and its adjacent side lobes increases with increasing surface fractal dimension (i.e. with increasing surface roughness). In this way a characterization of 3D fractal rough surfaces from backscattered SAR radar data is achieved, as explained in detail in this paper.DOI: http://dx.doi.org/10.5755/j01.eie.23.4.18721

Distribution and drivers of global mangrove forest change, 1996-2010.

For the period 1996-2010, we provide the first indication of the drivers behind mangrove land cover and land use change across the (pan-)tropics using time-series Japanese Earth Resources Satellite (JERS-1) Synthetic Aperture Radar (SAR) and Advanced Land Observing Satellite (ALOS) Phased Array-type L-band SAR (PALSAR) data. Multi-temporal radar mosaics were manually interpreted for evidence of loss and gain in forest extent and its associated driver. Mangrove loss as a consequence of human activities was observed across their entire range. Between 1996-2010 12% of the 1168 1°x1° radar mosaic tiles examined contained evidence of mangrove loss, as a consequence of anthropogenic degradation, with this increasing to 38% when combined with evidence of anthropogenic activity prior to 1996. The greatest proportion of loss was observed in Southeast Asia, whereby approximately 50% of the tiles in the region contained evidence of mangrove loss, corresponding to 18.4% of the global mangrove forest tiles. Southeast Asia contained the greatest proportion (33.8%) of global mangrove forest. The primary driver of anthropogenic mangrove loss was found to be the conversion of mangrove to aquaculture/agriculture, although substantial advance of mangroves was also evident in many regions.

Hybrid SAR/ISAR imaging of ship targets based on parameter estimation

ABSTRACTTypical Synthetic Aperture Radar (SAR) imaging deals with static targets. This means that moving targets appear blurred in SAR images. Difficulties in imaging are especially noticeable for moving ship targets at sea due to sea waves and each targets individual movement characteristics. Presently, there are no satisfactory results for hybrid SAR and Inverse Synthetic Aperture Radar (ISAR) imaging of multiple ship targets. Focussing on this problem, this letter proposes an improved airborne hybrid SAR/ISAR imaging algorithm based on parameter estimation. In this algorithm, Doppler parameters are estimated by using Radon-Wigner transform (RWT) at first. Then, we improve the traditional data extraction method, in which multi-objective problems are solved through data pre-processing and original characteristics of the data are preserved to the maximum extent. Three representative types of the ship data are selected in this letter and we extract them. Finally, effective ISAR motion compensation methods are adopted for the purpose of gaining focus results. The final images verify a strong feasibility of the proposed algorithm and it provides a good idea for moving ship targets imaging.

Characterization of Rough Fractal Surfaces from Backscattered Radar Data

In this paper the scattering of electromagnetic (EM) waves, emitted by a monostatic radar, from rough fractal surfaces is examined by using the Kirchhoff approximation. Of particular interest here is the way that the level of roughness of the fractal surface affects the backscattered EM wave captured by a synthetic aperture radar (SAR) and whether the roughness of the surface can be estimated from these SAR radar measurements. More specifically, the scattering coefficient of the backscattered signal is calculated for a number of radar frequencies and for different values of the surface fractal dimension. It is found here that the slopes between the main lobe and the first sidelobes emerging in the backscattering coefficient as a function of the wavenumber of the incident EM waves increase with the surface fractal dimension. Therefore, we conclude in this paper that the magnitude of the above slopes provides a reliable method for the classification of the rough fractal surfaces. Applications of the proposed method can be found, for example, in the characterization of the sea state from measured SAR radar data.DOI: http://dx.doi.org/10.5755/j01.eie.22.6.17226

Experimental Results of Passive SAR Imaging Using DVB-T Illuminators of Opportunity

In this letter, pioneering experimental results of passive synthetic aperture radar (SAR) imaging are presented. The classical active SAR radar operates in monostatic geometry. The SAR sensor presented in this letter is a passive radar utilizing commercial Digital Video Broadcasting—Terrestrial transmitters as illuminators of opportunity. It works in a bistatic configuration, where the receiver is placed on a moving platform and the transmitter is placed on the ground and is stationary. The imaged scenes are stationary surfaces on Earth such as agriculture or urban areas, buildings, etc. In this letter, pioneering results of signal processing verified by a measurement campaign are presented. In the experiment, two synchronized passive radar receivers were mounted on a small airborne platform. The main goal of the presented experiment was to verify the possibility of ground imaging using passive SAR technology and validate previously presented theoretical results.

A Generalized Approach for SAR and MIMO Radar Imaging of Building Interior Targets With Compressive Sensing

Most existing methods on compressive sensing (CS) for through-the-wall radar imaging (TWRI) are developed for monostatic synthetic aperture radar (SAR) and are only capable of target imaging behind a single-layer wall. In this letter, a generalized Green's function-based approach for the imaging of targets behind single- or multilayered building walls with CS is proposed. The approach is applicable to both SAR and multiple-input-multiple-output (MIMO) radar. By exploiting the sparsity of the target space, a less cluttered high-resolution image can be achieved with far fewer measurements. The number of antenna elements in the MIMO radar system can be significantly reduced using the proposed approach, resulting in an overall reduction of the complexity and cost of MIMO radar for TWRI applications. Numerical results are presented to show that high-quality focused image can be achieved under various wall-target scenarios for both SAR and MIMO radar.

Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images

In this paper the Modified Fractal Signature (MFS) method is applied to real Synthetic Aperture Radar (SAR) images provided to our research group by SET 163 Working Group on SAR radar techniques. This method uses the ‘blanket’ technique to provide useful information for SAR image classification. It is based on the calculation of the volume of a ‘blanket’, corresponding to the image to be classified, and then on the calculation of the corresponding Fractal Area curve and Fractal Dimension curve of the image. The main idea concerning this proposed technique is the fact that different terrain types encountered in SAR images yield different values of Fractal Area curves and Fractal Dimension curves, upon which classification of different types of terrain is possible. As a result, a classification technique for five different terrain types (urban, suburban, rural, mountain and sea) is presented in this paper. DOI: http://dx.doi.org/10.5755/j01.eee.20.6.7281

Automatic GCP Extraction of Fully Polarimetric SAR Images

This paper presents a method for automatic extraction of ground control points (GCPs) of fully polarimetric synthetic aperture radar (SAR) (PolSAR) images obtained from various satellites with different viewing angles. The scale-invariant feature transform (SIFT) algorithm is applied to extract candidate GCPs, where two-way keypoint matching eliminates improbable correspondence keypoints. Minimizing the root-mean-square error (rmse) also removes matching points with large rmse through a pseudoaffine transformation. In addition, information entropy and spatial dispersion quality constraints enable quantification of the spatial distribution of the GCPs. In accordance with full polarization, applying the SIFT-OCT algorithm (SIFT algorithm with the first scale-space octave skipped) to PolSAR data is examined. The total power (TP) image represents a combination of the characteristics of all four polarization images [horizontal transmitting and horizontal receiving (HH), horizontal transmitting and vertical receiving (HV), vertical transmitting and horizontal receiving (VH), and vertical transmitting and vertical receiving (VV)]. Therefore, GCP extraction using a TP image rather than each polarization image is proposed in order to maximize the accuracy of GCP extraction for all of the polarization data, as the TP image generates the highest signal-to-noise ratio (SNR) value. The SNR in conjunction with the matching correlation surface is used as an indicator of the reliability and accuracy of GCP extraction. After successfully applying the method to Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar and Japanese Earth Resources Satellite-1 SAR images, the GCP matching accuracy is further improved by using geometric calibration.