Resolution Synthetic Aperture Radar Images Research Articles

FMCW SAR System for Near-Distance Imaging Applications—Practical Considerations and Calibrations

A combination of frequency-modulated continuous-wave (FMCW) technology with a synthetic aperture radar (SAR) technique is a highly sought after method, which leads to a compact and cost-effective imaging system, for near-distance target imaging. One of the limiting factors of FMCW radars is that the ramp signal modulates the received signal, which limits the minimum achievable range resolution. In addition, the voltage-controlled oscillator (VCO) adds a certain degree of phase noise and nonlinearity to the transmitted signal that degrades the signal-to-noise ratio, range accuracy, and image resolution. This paper presents a signal processing procedure with system calibration methods to mitigate the effect of deramp, phase noise, and nonlinearity of the VCO on the beat spectrum. Additionally, the effect of phase noise of the received signal on the SAR image resolution in both range and cross-range directions is comprehensively studied. To improve the range accuracy, different calibration methods are proposed. An $S$ -band homodyne FMCW radar system, using off-the-shelf components, is designed for near-distance target imaging using linear and circular SAR techniques. The reconstructed images show the improvement of image quality and accuracy in target position.

A Hierarchical Split-Based Approach for Parametric Thresholding of SAR Images: Flood Inundation as a Test Case

Parametric thresholding algorithms applied to synthetic aperture radar (SAR) imagery typically require the estimation of two distribution functions, i.e., one representing the target class and one its background. They are eventually used for selecting the threshold that allows binarizing the image in an optimal way. In this context, one of the main difficulties in parameterizing these functions originates from the fact that the target class often represents only a small fraction of the image. Under such circumstances, the histogram of the image values is often not obviously bimodal and it becomes difficult, if not impossible, to accurately parameterize distribution functions. Here we introduce a hierarchical split-based approach that searches for tiles of variable size allowing the parameterization of the distributions of two classes. The method is integrated into a flood-mapping algorithm in order to evaluate its capacity for parameterizing distribution functions attributed to floodwater and changes caused by floods. We analyzed a data set acquired during a flood event along the Severn River (U.K.) in 2007. It is composed of moderate (ENVISAT-WS) and high (TerraSAR-X)-resolution SAR images. The obtained classification accuracies as well as the similarity of performance levels to a benchmark obtained with an established method based on the manual selection of tiles indicate the validity of the new method.

Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery

This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods.

Performance Analysis of TanDEM-X Quad-Polarization Products in Pursuit Monostatic Mode

Since 2010, the two twin synthetic aperture radar (SAR) satellites TerraSAR-X and TanDEM-X have been acquiring high-resolution images to generate a global Earth's digital elevation model (DEM). Both satellites have been flying in a controlled close orbit formation, acquiring data in the nominal bistatic stripmap single-polarization mode. Once the acquisition of the dataset for the generation of the DEM has been completed, the flexibility offered by both SAR instruments in terms of interferometric, imaging, and polarization modes has been further exploited to demonstrate the different capabilities of the TanDEM-X experimental modes. By activating the dual-receive antenna mode, full polarimetric data can be acquired. For the first time, it has been possible to systematically command quad-polarization acquisitions in a dedicated TanDEM-X mission science phase, started in October 2014. In this paper, we present a first performance analysis and quality assessment of such quad-polarization products. The SAR image resolution and the noise equivalent sigma zero have been evaluated to show the quality of the focused SAR products. The influence of different instrument parameters on the SAR and interferometric performance, such as chirp bandwidth, pulse repetition frequency, or block adaptive quantization, has been investigated as well. For the evaluation of the interferometric performance, key parameters such as coherence and interferometric phase error have been analyzed. In this paper, the obtained results are presented and recommendations are given for the optimization in the commanding of TanDEM-X quad-polarization acquisitions.

Directional feature: a novel feature for group target detection in high resolution SAR images

ABSTRACTIn this letter, we propose a new feature for group target detection in high resolution Synthetic Aperture Radar (SAR) images. This study aims to reduce the false alarm rate by adding a novel directional feature to typical SAR target detection algorithms. Unlike other shape- or contrast-based features, the directional feature contains the orientation and angle information of targets. Based on this feature, we can distinguish group targets from false alarms by analysing the correlation of their directional features. First, the proposed feature extraction approach generates an enhanced image to overcome speckle noises and a low signal to noise ratio (SNR). Next, a contour extraction algorithm is used to generate a line-drawing edge map of the target. Finally, the directional feature is obtained based on the major principal axes of the edge map. The experimental results using real high resolution SAR images verify the validity and effectiveness of the SAR detection algorithm.

Attribute Learning for SAR Image Classification

This paper presents a classification approach based on attribute learning for high spatial resolution Synthetic Aperture Radar (SAR) images. To explore the representative and discriminative attributes of SAR images, first, an iterative unsupervised algorithm is designed to cluster in the low-level feature space, where the maximum edge response and the ratio of mean-to-variance are included; a cross-validation step is applied to prevent overfitting. Second, the most discriminative clustering centers are sorted out to construct an attribute dictionary. By resorting to the attribute dictionary, a representation vector describing certain categories in the SAR image can be generated, which in turn is used to perform the classifying task. The experiments conducted on TerraSAR-X images indicate that those learned attributes have strong visual semantics, which are characterized by bright and dark spots, stripes, or their combinations. The classification method based on these learned attributes achieves better results.

中低分辨率合成孔径雷达影像多纹理特征的Otsu变化检测

中低分辨率合成孔径雷达影像多纹理特征的Otsu变化检测

COMPRESSED SYNTHETIC APERTURE RADAR IMAGING BASED ON MAXWELL EQUATION

Within few years backward, researches had presented the ability of compressive sensing to handle the large data problem on high resolution synthetic aperture radar (SAR) imaging. The main issue on CS framework that should be dealt with the SAR imaging is on the requirement of linearization on the measurement system. This paper proposes a new approach on formulating the compressed SAR echo imaging system which is derived from the Maxwell’s equations with continuous signal along the SAR antenna movement. Born approximation is applied to approximate the linear form of the SAR echo imaging system. In addition, the compressed sampling is formed by reducing the sampling rate of received radar signals randomly simultaneously on both of low sampling of fast time and slow time signals and by reducing the pulse period interval of transmitted signals. The simulation’s result shows that a better focused reconstructed sparse target can be achieved compared with the conventional match filter based Range Doppler (RD) method.

Land Surface Water Mapping Using Multi-Scale Level Sets and a Visual Saliency Model from SAR Images

Land surface water mapping is one of the most basic classification tasks to distinguish water bodies from dry land surfaces. In this paper, a water mapping method was proposed based on multi-scale level sets and a visual saliency model (MLSVS), to overcome the lack of an operational solution for automatically, rapidly and reliably extracting water from large-area and fine spatial resolution Synthetic Aperture Radar (SAR) images. This paper has two main contributions, as follows: (1) The method integrated the advantages of both level sets and the visual saliency model. First, the visual saliency map was applied to detect the suspected water regions (SWR), and then the level set method only needed to be applied to the SWR regions to accurately extract the water bodies, thereby yielding a simultaneous reduction in time cost and increase in accuracy; (2) In order to make the classical Itti model more suitable for extracting water in SAR imagery, an improved texture weighted with the Itti model (TW-Itti) is employed to detect those suspected water regions, which take into account texture features generated by the Gray Level Co-occurrence Matrix (GLCM) algorithm, Furthermore, a novel calculation method for center-surround differences was merged into this model. The proposed method was tested on both Radarsat-2 and TerraSAR-X images, and experiments demonstrated the effectiveness of the proposed method, the overall accuracy of water mapping is 98.48% and the Kappa coefficient is 0.856.

Detection of melt onset over the northern Canadian Arctic Archipelago sea ice from RADARSAT, 1997–2014

Abstract We present an algorithm to detect melt onset over Arctic sea ice using high resolution synthetic aperture radar (SAR) images from RADARSAT. The algorithm is based on the temporal evolution of the SAR backscatter coefficient (σ°), using an ice type specific threshold approach that also corrects for incidence angle variation. Using 4457 RADARSAT images, the algorithm was applied over sea ice in the northern Canadian Arctic Archipelago that low spatial resolution microwave sensors have difficulty resolving, thus generating a new time series of melt onset from 1997 to 2014. Over the 1997–2014 period, the mean annual average melt onset date in the northern CAA was on year day 164 (mid–June) with a standard deviation of 4 days. No significant trend for melt onset date was found over the 18-year period. An earlier (later) melt onset was associated with increased (decreased) solar energy absorption ( r = 0.64, significant at 95% confidence) and subsequently associated in lighter (heavier) September sea ice coverage in the northern CAA ( r = 0.62, significant at 95% confidence). RADARSAT estimates of melt onset were found to be in good agreement but more robust compared to passive microwave and scatterometer estimates over the northern CAA.

Improving the TanDEM-X Digital Elevation Model for flood modelling using flood extents from Synthetic Aperture Radar images

Abstract The topography of many floodplains in the developed world has now been surveyed with high resolution sensors such as airborne LiDAR (Light Detection and Ranging), giving accurate Digital Elevation Models (DEMs) that facilitate accurate flood inundation modelling. This is not always the case for remote rivers in developing countries. However, the accuracy of DEMs produced for modelling studies on such rivers should be enhanced in the near future by the high resolution TanDEM-X WorldDEM. In a parallel development, increasing use is now being made of flood extents derived from high resolution Synthetic Aperture Radar (SAR) images for calibrating, validating and assimilating observations into flood inundation models in order to improve these. This paper discusses an additional use of SAR flood extents, namely to improve the accuracy of the TanDEM-X DEM in the floodplain covered by the flood extents, thereby permanently improving this DEM for future flood modelling and other studies. The method is based on the fact that for larger rivers the water elevation generally changes only slowly along a reach, so that the boundary of the flood extent (the waterline) can be regarded locally as a quasi-contour. As a result, heights of adjacent pixels along a small section of waterline can be regarded as samples with a common population mean. The height of the central pixel in the section can be replaced with the average of these heights, leading to a more accurate estimate. While this will result in a reduction in the height errors along a waterline, the waterline is a linear feature in a two-dimensional space. However, improvements to the DEM heights between adjacent pairs of waterlines can also be made, because DEM heights enclosed by the higher waterline of a pair must be at least no higher than the corrected heights along the higher waterline, whereas DEM heights not enclosed by the lower waterline must in general be no lower than the corrected heights along the lower waterline. In addition, DEM heights between the higher and lower waterlines can also be assigned smaller errors because of the reduced errors on the corrected waterline heights. The method was tested on a section of the TanDEM-X Intermediate DEM (IDEM) covering an 11 km reach of the Warwickshire Avon, England. Flood extents from four COSMO-SKyMed images were available at various stages of a flood in November 2012, and a LiDAR DEM was available for validation. In the area covered by the flood extents, the original IDEM heights had a mean difference from the corresponding LiDAR heights of 0.5 m with a standard deviation of 2.0 m, while the corrected heights had a mean difference of 0.3 m with standard deviation 1.2 m. These figures show that significant reductions in IDEM height bias and error can be made using the method, with the corrected error being only 60% of the original. Even if only a single SAR image obtained near the peak of the flood was used, the corrected error was only 66% of the original. The method should also be capable of improving the final TanDEM-X DEM and other DEMs, and may also be of use with data from the SWOT (Surface Water and Ocean Topography) satellite.

Real-time implementation of frequency-modulated continuous-wave synthetic aperture radar imaging using field programmable gate array.

A new miniature linear frequency-modulated continuous-wave radar which mounted on an unmanned aerial vehicle is presented. It allows the accomplishment of high resolution synthetic aperture radar imaging in real-time. Only a Kintex-7 field programmable gate array from Xilinx is utilized for whole signal processing of sophisticated radar imaging algorithms. The proposed hardware architecture achieves remarkable improvement in integration, power consumption, volume, and computing performance over its predecessor designs. The realized design is verified by flight campaigns.

Multilayer CFAR Detection of Ship Targets in Very High Resolution SAR Images

This letter proposes a new ship target detection method for very high resolution (VHR) synthetic aperture radar (SAR) images based on multilayer constant false alarm rate (CFAR). First, combined with log-normal distribution, a multilayer CFAR method is designed to overcome the holes and the fracture in the traditional detected results. This method can retain more details of ships and takes much less time than the traditional CFAR method for VHR SAR images. Second, based on a priori knowledge of ships, we use the sliding window to remove the false alarm targets. Finally, In order to measure the size and shape of a ship, we extract the outline of a ship and fill it by a level set method. Experimental results, carried out on real SAR images, demonstrate that the proposed approach outperforms the previous one in terms of the detection ratio of pixels instead of the number of ships.

A New Region Growing-Based Method for Road Network Extraction and Its Application on Different Resolution SAR Images

Road network extraction plays an irreplaceable role in the applications of synthetic aperture radar (SAR) images. In this paper, we propose a new method based on the region growing to quickly extract the road network, which is suitable for different resolution SAR images. First, a weighted ratio line detector (W-RLD) is proposed to extract road features. Then, an automatic road seeds extraction method, which merges the ratio and direction information, is utilized to improve the quality of the extracted road seeds. Finally, the region growing concept is adopted to construct the road network, and a fast parameter selection procedure is presented for adaptively adjusting growing parameters. In experiments, four kinds of SAR images are used to assess the performance of the proposed method, including Envisat ASAR (30 m), HJ-1-C (5 m), TerraSAR-X (3 m), and airborne C-band data (0.5 m). Both visual and quantitative evaluation results show the adaptability and efficiency of the proposed approach.

PAR Model SAR Image Interpolation Algorithm on GPU with CUDA

ABSTRACTResolution of synthetic aperture radar (SAR) images is limited by imaging hardware. To improve SAR image resolution, interpolation needs to be carried out on a SAR image. An image interpolation algorithm with a good effect incurs a high computational cost and cannot be used in a real-time scenario. This paper applies Compute Unified Device Architecture (CUDA) to speed up the piece-wise autoregressive model image interpolation algorithm for acquiring high-quality SAR images. A partial differential is used to carry out image de-noising. After de-noising, divide the image interpolation based on a local window of a piece-wise autoregressive model: the SAR image is first divided into many 9 × 9 small local windows. For each window, a CUDA thread is launched to interpolate using the autoregressive interpolation algorithm. Gradient descent algorithm is used to estimate parameters of the autoregressive model in the first- and second-round interpolation. Numerical simulation indicates that this GPU-based parallel algorithm can interpolate a 2592 × 1944 image within 1/110th of the time used by a CPU-based serial algorithm. Moreover, the computation time saved increases with the image size. The experimental results show that the method in this paper can achieve high-quality image interpolation in a low computation time.

Processing of MEMPHIS Ka-Band Multibaseline Interferometric SAR Data: From Raw Data to Digital Surface Models

MEMPHIS is an experimental millimeter-wave synthetic aperture radar (SAR) system that acquires cross-track multibaseline interferometric data at high resolution in a single pass, using four receive horns. In this paper, we present the SAR system and navigation data, and propose a processing chain from the raw data input to a digital surface model (DSM) output. This processing chain includes full bandwidth reconstruction of the steppedfrequency SAR data, azimuth focusing with an Extended Omega-K algorithm, generation ofinterferograms for each available baseline, phase unwrapping using the multibaseline data, and phaseto-height conversion. The hardware and processing chain were validated through the analysis of experimental Ka-band data. The SAR image resolution was measured with point targets and found to be ~2% and ~15% coarser than the theoretical value in range and azimuth, respectively. The geolocation accuracy was typically better than 0.1 m in range and 0.2 m in azimuth. Observed depression angledependent interferometric phase errors were successfully removed using a correction function derived from the InSAR data. Investigation of the interferometric phase noise showed the utility of a multibaseline antenna setup; the number of looks and filter size used for the DSM generation were also derived from this analysis. The results showed that in grassland areas, the height difference between the ~2 m-resolution InSAR DSMs and the reference ALS models was 0 ± 0.25 m.

Power Spectra of Very High Resolution SAR Amplitude Images of Urban Areas

With the launch of COSMO/SkyMed and TerraSARX missions, very high resolution (VHR) Synthetic Aperture Radar (SAR) images of urban areas have become routinely available. In particular, in the spotlight acquisition mode, COSMO/SkyMed SARs are able to obtain a resolution even better than one meter. Accordingly, in principle a huge amount of information on objects present in the urban scenario can be extracted from such images; however, due to the involved interaction between incident electromagnetic wave and imaged scene, direct interpretation of VHR SAR images is not straightforward, and different methods to analyze them are being developed by the scientific community. In this paper, we explore the potentiality of using the power spectral density (PSD) of VHR SAR amplitude images of urban areas for information extraction. First, we propose a theoretical model of the PSD of such images, and then we test the validity of the developed model on COSMO/SkyMed spotlight SAR images of urban areas. Finally, some suggestions are provided on how the parameters of the obtained PSD can be exploited to extract useful information from VHR SAR amplitude images of urban areas.

SAR image Bayesian compressive sensing exploiting the interscale and intrascale dependencies in directional lifting wavelet transform domain

Compressive Sensing (CS) provides a new solution to reduce the huge amount of data for the transmission and storage of high resolution synthetic aperture radar (SAR) images. To improve the CS perfo...

Change Detection and Analysis of High Resolution Synthetic Aperture Radar Images Based on Bag-of-words Model

Change Detection and Analysis of High Resolution Synthetic Aperture Radar Images Based on Bag-of-words Model

SAR image Bayesian compressive sensing exploiting the interscale and intrascale dependencies in directional lifting wavelet transform domain

Compressive Sensing (CS) provides a new solution to reduce the huge amount of data for the transmission and storage of high resolution synthetic aperture radar (SAR) images. To improve the CS performance, in this work we propose directional lifting wavelet transform (DLWT) as a sparse representation for SAR image CS. Then a Bayesian-based SAR image CS reconstruction algorithm in DLWT domain is proposed. To further improve the reconstruction performance, an accurate prior probability model is proposed which fully exploits interscale attenuation and intrascale directional clustering properties of the DLWT coefficients; and a Bayesian inference via Markov Chain Monte Carlo (MCMC) sampling is employed to recover the image׳s wavelet coefficients and SAR image. Experimental results show that the proposed DLWT Tree-Direction-Clustering Compressive Sensing (DLWT-TDC-CS) can achieve the best reconstruction performance at sampling rates from 0.5 to 0.9 compared with various state-of-the-art CS reconstruction algorithms. DLWT-based CS reconstruction outperforms DWT-based CS reconstruction due to the improved sparse representation.