Sparse synthetic aperture radar imaging with optimized azimuthal aperture

To counter the problem of acquiring and processing huge amounts of data for synthetic aperture radar(SAR)using traditional sampling techniques,a method for sparse SAR imaging with an optimized azimuthal aperture is presented.The equivalence of an azimuthal match filter and synthetic array beamforming is shown so that optimization of the azimuthal sparse aperture can be converted to optimization of synthetic array beamforming.The azimuthal sparse aperture,which is composed of a middle aperture and symmetrical bilateral apertures,can be obtained by optimization algorithms(density weighting and simulated annealing algorithms,respectively).Furthermore,sparse imaging of spectrum analysis SAR based on the optimized sparse aperture is achieved by padding zeros at null samplings and using a non-uniform Taylor window.Compared with traditional sampling,this method has the advantages of reducing the amount of sampling and alleviating the computational burden with acceptable image quality.Unlike periodic sparse sampling,the proposed method exhibits no image ghosts.The results obtained from airborne measurements demonstrate the effectiveness and superiority of the proposed method.     

Submarine groundwater discharge in tidal flats revealed by space-borne synthetic aperture radar

Most space-borne sensors cannot detect subsurface features. Groundwater is a typical subsurface feature, and its discharge to coastal ocean waters plays an important role in transporting terrestrial chemical constituents and providing habitats for various species of fauna and flora. This is the first paper to report observational evidence for submarine groundwater discharge (SGD) in tidal flats using space-borne synthetic aperture radar (SAR). Tidal flats are composed of high-moisture-saturated sediments and water puddles. These shallow water puddles were imaged effectively by using SAR systems. The presence of water puddles is usually indicated by low radar backscatter in SAR images due to specular reflections on the water surface. This effect was proved by comparing radar backscattering coefficients obtained from two space-borne SAR systems, TerraSAR-X and RADARSAT-2, with those obtained from two theoretical scattering models, IEM and Oh model. We observed relatively large, widely distributed water puddles in belt shape along the upper parts of the tidal flat, which were confirmed to be related to the discharge of groundwater. The results of this research suggest that SAR can be a powerful tool for observing and determining the areal distributions of possible groundwater discharge in large tidal flats, which is normally difficult to detect with traditional measurement tools or survey techniques for groundwater discharge. We firmly believe that this technique can reduce significantly the efforts of field work to confirm SGD in tidal flats.     

Sea state in the Baltic Sea from space-borne high-resolution synthetic aperture radar imagery

In this work, remote sensing synthetic aperture radar (SAR) data from X-band TerraSAR-X and TanDEM-X (TS-X and TD-X) satellites have been used to adopt the algorithms for estimating sea state parameters in the specific condition of the Baltic Sea with archipelago islands and where short steep sea state dominates. Since the moving targets can be defocused and shifted in SAR images, sea state consisting of short windsea waves with strong local orbital velocities and wave breaking needs additional effort for accurate estimation of the total significant wave height that consists of swell and windsea parts. The XWAVE_C algorithm, developed for the North Sea, where the long swell waves coming from the Atlantic Ocean are present during storms, was further enhanced for the short steep windsea which dominates under ordinary storm conditions in the Baltics. For the empirical XWAVE_C model function, based on the spectral analysis of subscenes as well as on local wind information, an additional term was incorporated for assessment the minimal windsea significant wave height by applying JONSWAP wave spectra. A term to compensate spectral distortions triggered by windsea waves moving in SAR flight direction has also been introduced. In total, 95 TS-X/TD-X StripMap scenes between 2012 and 2017 were acquired in Eastern Baltic Sea, processed and analysed. The wave height results from SAR images were compared with collocated in situ data from 11 available buoys. The analysed data include both high and low windsea conditions. The comparison of SAR-derived wave heights with measured wave heights shows high agreement with a correlation coefficient r of 0.88. The wind speed, estimated from SAR images, was compared to measurements from 14 collocated in situ stations, yielding a high agreement with an r value of 0.90. This article is focused on the algorithm developments; however, it is also the first study of sea state retrieval in the Baltic Sea using high-resolution satellite-based techniques. The results show the local variability in the wave fields connected to atmospheric features. The observed local wave height can increase by 1–2 m in kilometre-size cells that are accompanied by wind gusts. The developed algorithms are installed in the German Aerospace Center’s (DLR) ground station Neustrelitz and can also be used in near-real-time.     

Cover: Detection of oil spills near offshore installations using synthetic aperture radar (SAR)

The number of sunspots is characterized by a long-term temporal variation, reaching its maximum or its minimum approximately every 11 years (the solar cycle). This variation, in turn, has an effect in terms of variation in the global climate. Since 1979, the use of satellite-borne radiometers has enabled accurate measurements of total solar irradiance (TSI). For instance, the sunspot numbers that are scaled to correspond to Nimbus-7 TSI observations for 1979–1993 show little long-term trend. However, while the observations of different extremes of the solar cycle, which are available from 1749, seem irregular in time and magnitude and difficult to quantify, they appear to have a strong correlation between them when they are sorted pairwise according to their size rather than sequentially in time. A similar relation holds among the solar cycle lengths (periods) and the solar cycle extremes, which, in parallel, obey a linear relation that is reminiscent of the Gutenberg–Richter seismic law. This can be used for a probabilistic approach to forecast solar parameters that are connected to global climate.

Based on the reasonable assumption that the basic parameters like extremes and length of the 11-year solar cycle are associated with the energy oscillating between the dipolar and quadrupolar phases of the cycle, it is concluded that these parameters obey a power-law distribution similar to that of the Gutenberg–Richter seismic law.

The question of whether solar activity is deterministically chaotic is also investigated by exploring the behaviour of the main characteristics of the 11-year solar cycle. This is done by constructing return maps of solar cycle strength and duration, which seem to take the familiar up–down U shape, implying both non-linearity and re-injection. The results suggest that there might exist a coupling between two or three different non-linear deterministic dynamical systems on the Sun, depending on which variable of the solar cycle is being considered.     

An improvement method for circular synthetic aperture radar imaging

Circular synthetic aperture radar (CSAR) is the imaging mode when the radar moves along a circular path and the observed area is always covered by the wave beam. It is different from traditional SAR modes (strip-map SAR and spotlight SAR) and has potential advantages such as 360° observation, target recognition, and three-dimensional reconstruction. According to the imaging processing of CSAR, motion error is an important issue affecting the CSAR image quality, but the motion compensatio n (MOCO) method for CSAR is underdeveloped. Accordingly, with detailed analysis the motion error model is established and a data-driven MOCO flow chart for CSAR is proposed. The real CSAR data are used to verify the proposed method.     

Multi-look processing of synthetic aperture radar data from dynamic ocean surfaces

The basic theory is described of multi-look processing of synthetic aperture radar (SAR) data from dynamic ocean surfaces. The principal effects inherent to multi-look SAR on the impulse response function are first illustrated using a moving point target model. The theory is then extended to the multi-look imagery of diffusely scattering ocean surface waves. Finally, the use of a sliding synthetic aperture is suggested to investigate the short life-times of small scale surface waves that are considered to be the predominant scatterers on the ocean surface.     

A feature extraction method for synthetic aperture radar(SAR) automatic target recognition based on maximum interclass distance

Synthetic aperture radar(SAR) automatic target recognition is an important application in SAR.How to extract features has restricted the application of SAR technology seriously.In this paper,a new feature extraction method for SAR automatic target recognition based on maximum interclass distance is proposed,which integrates class and neighborhood information.This method can reinforce discriminative power using maximum interclass distance,so it can improve recognition rate effectively.     

Processing synthetic aperture radar data†

Abstract

Basic operational principles of synthetic aperture radar systems are reviewed, with an emphasis on the data processing requirements. The techniques of image formation for these systems are covered, using either optical or digital techniques. Examples of imagery are shown to illustrate the data processing results. SAR processing capabilities as they exist worldwide are summarized.     

Cover. Wind energy mapping of coastal zones by synthetic aperture radar (SAR) for siting potential windmill locations

MERIS (Medium Resolution Imaging Spectrometer) is a fine spectral and medium spatial resolution satellite sensor and is part of the core instrument payload of Envisat, the European Space Agency's (ESA) environmental research satellite, launched in March 2002. Designed primarily for ocean (‘MER’) and coastal zone remote sensing, this imaging spectrometer (‘IS’) now has a much broader environmental remit covering also land and atmospheric applications. This paper reviews (i) MERIS's development history, focusing on its changing mission objectives; (ii) MERIS's technical specification, including its radiometric, spectral and geometric characteristics, programmability and onboard calibration; (iii) decisions that led to modifications of MERIS's spectral, geometric and radiometric performance for land applications; (iv) MERIS's data products; and (v) some of the ways in which MERIS data might be used to provide information on terrestrial vegetation.     

A model comparison study to the imaging of submarine reefs with synthetic aperture radar

Signatures of submarine reefs near Heligoland in the North Sea were observed in airborne radar images recorded at L-, C- and X-bands on 14 November 1990 during rather high wind speed of 9 ms^-1. Predictions from various models of the imaging mechanism were compared to these observations. One of the models is the so-called weak hydrodynamic interaction theory (WHIT) model. It is fully two-dimensional in position as well as wavenumber space, so any surface current variation can be handled. Also more sophisticated scattering models than first-order Bragg scattering can be included. The model contains a number of parameterizations for the roughness length, the equilibrium wave height spectrum and the relaxation rate as well as different forms for the local relaxation source term. In the model intercomparison, the WHIT model performed not very well. It is shown here that this is due to the choice of the radial relaxation rate. In a sensitivity analysis it is shown that also the form of the relaxation source term is important. A linear source term may lead to unrealistically high positive hydrodynamic modulations (up to 50 dB) at some positions over the reefs for waves with a wavelength of about 0.6 m. Such effects do not occur in quadratic or cubic source terms, which are therefore to be preferred. The parameterizations chosen for the roughness length and the angular relaxation rate have little influence on the model results. Also shoaling may be neglected. A scattering model based on first iteration of the Stratton-Chu equation gives results similar to that of an improved two-scale model. When compared to the observations, good agreement is obtained at L-band, but at C- and X-bands the model underestimates the modulations. A number of possible causes is discussed, but additional data are needed to settle this question.     

Compressive sensing inverse synthetic aperture radar imaging based on Gini index regularization

In compressive sensing (CS) based inverse synthetic aperture radar (ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar (ISAR) imaging. Different from the traditional l ₁ norm based CS ISAR imaging method, our method explores the use of Gini index to measure the sparsity of ISAR images to improve the imaging quality. Instead of simultaneous perturbation stochastic approximation (SPSA), we use weighted l ₁ norm as the surrogate functional and successfully develop an iteratively re-weighted algorithm to reconstruct ISAR images from compressed echo samples. Experimental results show that our approach significantly reduces the number of measurements needed for exact reconstruction and effectively suppresses the noise. Both the peak sidelobe ratio (PSLR) and the reconstruction relative error (RE) indicate that the proposed method outperforms the l ₁ norm based method.     

Multi-chromatic analysis polarimetric interferometric synthetic aperture radar (MCA-PolInSAR) for urban classification

One of the problems of Synthetic Aperture Radar (SAR) polarimetric decomposition, is that oriented urban areas and vegetation signatures are decomposed into the same volume scattering mechanism. Such indetermination makes it difficult to distinguish vegetation from the oblique urban areas with respect to the radar illumination direction within the volume scattering mechanism. This event occurs because oriented targets exhibit similar polarimetric responses. This paper presents an improvement of the PolSAR decomposition scheme which permits the performing of more accurate classification. The method uses the information existing form the interference generated between two Doppler sub-aperture SAR images. This interferometric polarimetric SAR (PolInSAR) multi-chromatic analysis (MCA-PolInSAR) signal processing method permits the efficient separation of oriented buildings from vegetation yielding considerably improved results in which oriented urban areas are recognized, from volume scattering, as double-bounce objects. Results also show a considerable improvement in the robustness of classification and also in terms of definition and precision.     

Multi-look versus single-look processing of synthetic aperture radar images with respect to ocean wave spectra estimation

Abstract

Full-bandwidth C-band synthetic aperture radar (SAR) data are compared with 7-look and 3-look data. The peak-to-background ratio of the image intensity power spectrum describing the wave detectability is found to be on average 8-9dB higher for the 7-look data and 2-5dB higher for the 3-look data than the single-look data. This is mainly due to the decrease in the speckle noise level when going from single-look to multi-look processing. In addition, look-sum processing is evaluated against spectral-sum processing for various temporal look separations. A significant improvement in image spectral peak contrast is observed for the spectral-sum data versus the look-sum data, with increasing temporal separations between the looks. No such improvement is observed in the corresponding image spectral noise contrast parameter. These observations are in agreement with the spatial misregistration inherent in look-sum data. Finally, the acceleration contribution to the observed aximuth smearing in the spectra is found to be negligible compared with the velocity smearing contribution.     

高炉燃烧料面合成孔径雷达双聚焦成像算法

钢铁生产中的料位和料形是冶炼过程控制的重要指标,基于合成孔径雷达(SAR)原理的微波成像装置可有效提取热态径向料面特征.针对几何形态复杂,回波信号突变性强,甚至会出现遮蔽效应导致雷达失波的料面中心部分,本文建立了中心区域双聚焦测量成像系统.选定炉顶风罩位置对称安装两套雷达,电机控制雷达天线匀速摆动获取二维坐标矩阵,用重采样插值算法将其转换成像素点均匀的矩形灰度图像;根据尺寸参数确定炉心扫描重叠区间,并构建料面中心双聚焦融合算法(DfSAR)拼接双图像.与均值融合相比,图像互信息提高10.43%,清晰度改善23.91%.最后,采用雷达恒虚警率(CFAR)目标检测算法有效滤除高炉内粉尘颗粒物对料面图像的干扰.     

Simplified radar mapping equations for terrain correction of space-borne SAR images

Correction of a space-borne Synthetic Aperture Radar (SAR) image for the effects of terrain distortion requires the use of mapping equations that relate the coordinates of a scatterer in three dimensions to its position in the twodimensional image. These mapping equations are complicated since they take into account the curved geometry of the Earth's surface, and since they must be evaluated of the order of 10 to 100 million times in the course of correcting a typical SAR image, the complexity is a disadvantage. In this paper we derive two approximations, one quadratic and one linear, to the mapping equation, and evaluate their performance relative to the observational parameters of a variety of space-borne SAR systems and the range of topographic variation present in the scene. We show that the quadratic approximation is sufficiently accurate in virtually all circumstances likely to be encountered. In most cases the linear approximation is also valid, although it is unsuitable where the topographic variation is large and the near-swath incidence angle is small.     

Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173× acceleration on the training samples, and a 181× acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.     

Terrain height measurement by synthetic aperture radar with an interferometer

This paper describes the terrain height measurement capability of synthetic aperture radar with an interferometer assuming the radar operates on a satellite. The interferometer phase measurement uncertainty due to additive thermal noise and multiplicative speckle noise, which determine the upper limit of the height measurement accuracies of this radar system, has been obtained by numerical simulation. The height measurement accuracies attainable on a relatively flat terrain areas are of roughly the same order as range resolution when C- or X- band radar is operated at an altitude of 300 km.     

Urban land use classification using synthetic aperture radar

Abstract

This paper presents several approaches to the use of radar imagery for land use classification of urban and near-urban areas. The use of L(HH) (L band, horizontal transmit and horizontal receive) data is emphasized because it is these types of data obtained by Seasat-A (and in November 1981 by Shuttle radar) which are most generally available. For urban area studies using imaging radar the effect of processing in an off-zero doppler (‘squint’) mode, the presence of large diffuse scatters and the possibility of height measurements are discussed. Each approach provides information and also requires supporting ground truth which are unique to radar remote sensing. For some areas the coupling of data from the microwave portion of the spectrum to the data available in the visible and near visible realms may improve the classification of urban and near-urban land use. However, the radar data are not without their own limitations which may be imposed by either the system or the nature of the imaged scene. A proper knowledge of these limitations can permit us to turn a perceived defect into a decided advantage. The metropolitan area of Los Angeles provides the geographic background for this study.     

SRAD-CNN for adaptive synthetic aperture radar image classification

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.