Fully Polarimetric SAR Ship Detection based onPCDM Shannon Entropy#br#

Polarimetric Synthetic Aperture Radar（PolSAR）data contains rich polarization information about the scattering properties of ground objects,having beenwidely used in maritime monitoring and objects detection.The polarization reaction differences between ship targets and sea clutters are analyzed.A ship detection method using the Shannon entropy of the Polarimetric Covariance Difference Matrix (PCDM) is proposed in this paper,which is applied to fully polarimetric SAR images.To enhance the contrast between the ship targets and sea background,the PCDM is generated by calculating the elemental differences between the polarimetric covariance matrix at each pixel and its neighbors.Then the Shannon entropy of SAR images are extracted on the basis of the Shannon entropy calculation formula,and the character difference between the ships and background in the Shannon entropy map is presented for ship detection.The false alarms in the detection result caused by the azimuth ambiguities are removed,based on the displacement distance and energy ratio relationship,between the target and azimuth ambiguity.The Radarsat\|2 Fine Quad data and the Chinese GF\|3 Quad\|Polarimetric Stripmap Ⅰ data are used,to verify the effectiveness of the proposed method,and the SPAN method,HV channel image and polarimetric whitening filter (PWF) method are applied for comparison.The detection and comparison results indicate that the proposed method is able to effectively enhance the ship\|sea contrast,and has higher detection accuracy.     

Feature selection and weighted SVM classifier-based ship detection in PolSAR imagery

Target decomposition is an important method for ship detection in polarimetric synthetic aperture radar (SAR) imagery. Parameters such as the polarization entropy and alpha angle deduced from the coherency matrix eigenvalue decomposition capture the differences between the target and background from different views separately. However, under the conditions of a relatively high resolution and a rough sea, the contrast between ship and sea reduces in the aforementioned space. Based on the analyses of target decomposition theory and the target’s scattering mechanism, multi-polarization parameters can be used to characterize different scattering behaviours of the ship target and sea clutter. Moreover, each parameter has its own diverse significance in the practical detection problem. This article proposes a feature selection and weighted support vector machine (FSWSVM) classifier-based algorithm to detect ships in polarimetric SAR (PolSAR) imagery. First, the method constructs a feature vector that consists of multi-polarization parameters. Then, different polarization parameters are refined and weighted according to their significance in the support vector machine (SVM) classifier. Finally, ships are classified from the sea background and other false alarms by the classifier. The validation results on National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) airborne synthetic aperture radar (AIRSAR) and Radarsat-2 quad polarimetric data illustrate that the method detects ship targets more precisely and reduces false alarms effectively.     

改进Notch滤波的全极化SAR数据船舶检测方法

全极化SAR数据提供了更多的地物极化散射信息,目前被广泛的应用于海上船舶检测的应用研究。本文提出改进的Notch滤波方法,实现全极化SAR数据的海上船舶检测。该方法结合目标的极化散射特性与能量双重特点,设计针对海面、方位向模糊、相干斑噪的不同滤波,消除虚警,通过SPAN能量因子降低由于散射机制相同而造成的漏检。利用RADATSAT-2全极化精细扫描数据对本文的算法进行验证,并与PWF和SPAN方法进行对比分析,实验结果表明本文提出的方法能从海面上有效检测出各种大小的船舶,同时能抑制方位向模糊、相干斑噪以及船舶的旁瓣造成的虚警。     

星载SAR影像上船舶方位向模糊去除算法

目的 掌握海上船舶分布状态对于海上交通流分析和通航安全管理具有重要作用。遥感技术,特别是星载合成孔径雷达(SAR)技术的发展,为大范围海上船舶检测提供了有效的手段,但受SAR成像机制影响,海上船舶目标在星载SAR影像上通常存在着不同程度的方位向模糊噪声,这些噪声易被误判为船舶,导致船舶识别中虚警率提高。方法 本文简述了方位向模糊噪声的产生原因,提出了一种新的星载SAR影像上船舶方位向模糊去除算法,该算法的核心是构建目标方位向角度一致性、方位向位置偏移距离和方位向模糊能量衰减3个判别规则,对潜在SAR影像亮斑目标进行逐层筛选,实现船舶真实目标和方位向模糊目标的判别。结果 选取中国渤海海域和黄海海域的30 m分辨率的Radarsat-2数据进行案例分析,并与船舶自动识别系统(AIS)实测数据进行比对校验,结果表明,传统的双参数恒虚警率(CFAR)算法和基于K分布的CFAR等算法对于船舶难以剔除方位向模糊,容易造成虚警,而本文算法对实验影像的船舶方位向模糊去除准确率优于95.8%,能够有效剔除船舶方位向模糊。结论 该算法为星载SAR影像上船舶方位向模糊去除提供了新的手段,有助于提高SAR影像上船舶目标检测的准确性。     

Investigation of the effect of the incidence angle on land cover classification using fully polarimetric SAR images

Incidence angle is one of the most important imaging parameters that affect polarimetric SAR (PolSAR) image classification. Several studies have examined the land cover classification capability of PolSAR images with different incidence angles. However, most of these studies provide limited physical insights into the mechanism how the variation of incidence angle affects PolSAR image classification. In the present study, land cover classification was conducted by using RADARSAT-2 Wide Fine Quad-Pol (FQ) images acquired at different incidence angles, namely, FQ8 (27.75°), FQ14 (34.20°), and FQ20 (39.95°). Land cover classification capability was examined for each single-incidence angle image and a multi-incidence angle image (i.e., the combination of single-incidence angle images). The multi-incidence angle image produced better classification results than any of the single-incidence angle images, and the different incidence angles exhibited different superiorities in land cover classification. The effect mechanisms of incidence angle variation on land cover classification were investigated by using the polarimetric decomposition theorem that decomposes radar backscatter into single-bounce scattering, double-bounce scattering and volume scattering. Impinging SAR easily penetrated crops to interact with the soil at a small incidence angle. Therefore, the difference in single-bounce scattering between trees and crops was evident in the FQ8 image, which was determined to be suitable for distinguishing between croplands and forests. The single-bounce scattering from bare lands increased with the decrease in incidence angles, whereas that from water changed slightly with the incidence angle variation. Consequently, the FQ8 image exhibited the largest difference in single-bounce scattering between bare lands and water and produced the fewest confusion between them among all the images. The single- and double-bounce scattering from urban areas and forests increased with the decrease in incidence angles. The increase in single- and double-bounce scattering from urban areas was more significant than that from forests because C-band SAR could not easily penetrate the crown layer of forests to interact with the trunks and ground. Therefore, the FQ8 image showed a slightly better performance than the other images in discriminating between urban areas and forests. Compared with other crops and trees, banana trees caused stronger single- and double-bounce scattering because of their large leaves. As a large incidence angle resulted in a long penetration path of radar waves in the crown layer of vegetation, the FQ20 image enhanced the single- and double-bounce scattering differences between banana trees and other vegetation. Thus, the FQ20 image outperformed the other images in identifying banana trees.     

PolSAR ship detection based on the contrast enhancement of polarimetric scattering differences

The ship detection in polarimetric synthetic aperture radar (PolSAR) mode is a hot topic in recent years, because of the diversity of polarimetric scattering mechanisms between ship targets and sea clutter. To improve the detection performance of ship targets, this paper mainly develops the ship detection method based on the contrast enhancement utilizing the polarimetric scattering difference. The algorithm first enhances the target signal utilizing the scattering difference of the polarimetric coherency matrix between ship targets and sea clutter, and then a simple threshold is applied to distinguish the ship targets from the sea clutter. Finally, real PolSAR datasets recorded by AirSAR system are used to evaluate the effectiveness of the proposed detection method. Compared with other detection methods, experimental results indicate that the proposed method can effectively improve the detection performance of ship targets.     

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.     

Application of a three-component scattering model over snow-covered first-year sea ice using polarimetric C-band SAR data

In this study we examine the utility of a three-component scattering model to quantify the sensitivity of radar incidence angle over snow-covered landfast first-year sea ice (FYI) during the late winter season. This three-component scattering model is based on (1) surface scattering contributed from the snow-covered FYI (smooth-ice (SI), rough-ice (RI), and deformed-ice (DI) types); (2) volume scattering contributed from snow layers which consist of enlarged snow grains, elevated brine volume, and preferential orientation of snow grains relative to radar look direction, as well as the underlying sea ice; and (3) double-bounce scattering contributed from ice ridges and ice fragments. This study uses RADARSAT-2 C-band polarimetric synthetic aperture radar (POLSAR) data acquired on 15 and 18 May 2009 for Hudson Bay, near Churchill, during late winter with surface air temperatures ≤?8°C at two different incidence angles (29° and 39°). The three-component scattering model is used to discriminate between snow-covered smooth, rough, and deformed FYI. The model shows enhanced discrimination at an incidence angle of 29°, compared with an incidence angle of 39°. The model is then used to quantify the sensitivity of radar incidence angle to each of the three scattering contributors. The results show that the relative fraction of surface scattering dominates for all three FYI types (SI ≈ 77.3%; RI ≈ 66.0%; and DI ≈ 61.1%) at 29° and decreases with increasing incidence angle and surface roughness. Volume scattering is found to be the second dominant mechanism (SI ≈ 19.1%, RI ≈ 32.2%, and DI ≈ 37.4% at 29° and SI ≈ 28.3%, RI ≈ 41.0%, and DI ≈ 49.5% at 39°) over snow-covered FYI and it increases with incidence angle and surface roughness. The double-bounce scattering contribution is low for all FYI types at both incidence angles.     

Radar detection of flooding beneath the forest canopy: a review

Abstract

Synthetic aperture radar remote sensing is a promising tool for detection of flooding on forested floodplains. The bright appearance of flooded forests on radar images results from double-bounce reflections between smooth water surfaces and tree trunks or branches. Enhanced back scattering at L-band has been shown to occur in a wide variety of forest types, including cypress-tupelo swamps, temperate bottomland hardwoods, spruce bogs, mangroves and tropical floodplain forests. Lack of enhancement is a function of both stand density and branching structure. According to models and measurements, the magnitude of the enhancement is about 3 to 10 dB. Steep incidence angles (20^°-30^°) are optimal for detection of flooding, since some forest types exhibit bright returns only at steeper angles. P-band should prove useful for floodwater mapping in dense stands, and multifrequency polarimetric analysis should allow flooded forests to be distinguished from marshes.     

An extended multiple-component scattering model for PolSAR images

An extended multiple-component scattering model (MCSM) is proposed for polarimetric synthetic aperture radar (PolSAR) image decomposition. The MCSM is an extension of the three-component scattering model (TCSM), and it describes single-bounce, double-bounce, volume, helix and wire scattering as elementary scattering mechanisms in the analysis of PolSAR images. The proposed MCSM is demonstrated with German Aerospace Centre (DLR) experimental SAR (ESAR) L-band fully polarized images of the Oberpfaffenhofen Test Site Area (DE), Germany. Double-bounce, helix and wire scattering are found to be predominant in urban areas and the results confirm that the MCSM is effective for analysis of buildings in urban areas. A comparison of the TCSM and its extended models is also implemented.     

Discrimination potential of X-band polarimetric SAR data

In anticipation of X-band polarimetric Synthetic Aperture Radar (SAR) data from future sensors, we investigated the potential of X-band fully polarimetric data for discriminating between the principal classes present in a study site near Avignon, France. Decomposition and analysis techniques have been applied to a dataset acquired by the ONERA airborne RAMSES (Radar Aéroporté Multi-Spectral d'Etude des Signatures) SAR. Results show that X-band provides some discrimination capability. The polarimetric parameters, entropy and α-angle, show clearly that these signature classes are grouped into five clusters corresponding to physical scattering characteristics. The introduction of the anisotropy parameter does not increase our ability to distinguish between different classes whose clusters are in the same entropy/α-angle zone. The correlation observed between the radar signal and the surface roughness over bare soils is very low.     

Anisotropic analysis of polarimetric scattering and case studies with UAVSAR images

This article analyses the anisotropy of polarimetric scattering changing with azimuth incidence angle using a multi-look processed synthetic aperture radar (SAR) image. First, three canonical scattering models were developed to simulate the migration tracks on the Cameron polarimetric space. The migration tracks indicate that these polarimetric parameters have anisotropic property. Second, unmanned aerial vehicle synthetic aperture radar (UAVSAR) data are used to validate the simulated results. The Cameron scattering-type parameter z and the orientation angle calculated by SAR data are consistent with the simulated results by small perturbation method (SPM) double-scattering. Finally, based on the anisotropic analysis, a new method of extracting polarimetric information is proposed. Using this method, six parameters were obtained and two additional parameters, Purity and Stability, were derived. These parameters contain specific physical meaning and are useful in the recognition of the scattering mechanism. Purity can be used to recognize the simple structure scatterers with zero orientation. Stability has the potential to describe the dynamic property of scatterers.     

基于星载宽刈幅散射计的海面舰船目标检测方法与识别

针对全球海面舰船目标检测的需求,结合宽刈幅与方位高分辨的需求,提出了一种基于星载宽刈幅散射计的海面舰船目标检测方法.该方法使用扇形波束旋转扫描散射计,发射宽带线性调频信号,对回波信号进行方位高分辨,海面目标二维检测,得到舰船的多次观测信息.使用方位角、多普勒频率、高分辨距离像等观测数据,计算船长、船速、与卫星运动方向夹...     

A new method for extracting ship motion parameters in Radarsat-2 SAR imagery

Traditionally, moving ship detection by Synthetic Aperture Radar (SAR) image is primarily based on the ship wake feature. However, many ship wakes cannot be imaged by SAR owing to changes in imaging conditions, such as the SAR band, polarisation, incident angle, and sea state. In this study, we discovered a unique phenomenon called ‘azimuth tail’ from Radarsat-2 SAR images. Following research and analysis, we determined that the azimuth tail is not manifested as a visible disturbance on sea surface waves. Instead, it is an observation enabled by certain SAR imaging principles. Consequently, we propose a new method for extracting information on moving vessels after a preliminary analysis of the principle of the azimuth tail. The results of experimental analysis of the correctness of the method indicate that the error of the vessel’s velocity from its azimuth tail is less than 20%, and the azimuth tail can be applied to the detection of moving vessels in oceans using Radarsat-2 SAR imagery.     

Integrated analysis of PALSAR/Radarsat-1 InSAR and ENVISAT altimeter data for mapping of absolute water level changes in Louisiana wetlands

Interferometric Synthetic Aperture Radar (InSAR) has been used to detect relative water level changes in wetlands. We developed an innovative method to integrate InSAR and satellite radar altimetry for measuring absolute or geocentric water level changes and applied the methodology to remote areas of swamp forest in coastal Louisiana. Coherence analysis of InSAR pairs suggested that the HH polarization is preferred for this type of observation, and polarimetric analysis can help to identify double-bounce backscattering areas in the wetland. ENVISAT radar altimeter-measured 18-Hz (along-track sampling of 417 m) water level data processed with regional stackfile method have been used to provide vertical references for water bodies separated by levees. The high-resolution (~ 40 m) relative water changes measured from ALOS PALSAR L-band and Radarsat-1 C-band InSAR are then integrated with ENVISAT radar altimetry to obtain absolute water level. The resulting water level time series were validated with in situ gauge observations within the swamp forest. We anticipate that this new technique will allow retrospective reconstruction and concurrent monitoring of water conditions and flow dynamics in wetlands, especially those lacking gauge networks.     

On the exploitation of polarimetric SAR data to map damping properties of the Deepwater Horizon oil spill

A polarimetric scattering model is proposed to exploit quad-polarimetric synthetic aperture radar (SAR) data to both observe surfactants at sea and provide the first information on the spatial variability of their damping properties. The model is based on the departure from the clean sea surface Bragg/tilted Bragg scattering mechanism. This departure is shown to be a function of the surfactant’s characteristics, and therefore, it is exploited to map them. Case studies of polarimetric SAR data collected during the Deepwater Horizon oil spill in Gulf of Mexico are examined. The approach is robust enough to successfully exploit both L-band airborne and C-band satellite SAR data. This is of paramount importance, even operationally, since it makes this physical approach cross-sensors and, therefore, suitable to exploit all the operational polarimetric missions, thus allowing a denser spatial/temporal coverage.     

Measurement of ocean surface slopes and wave spectra using polarimetric SAR image data

Methods have been investigated which use fully polarimetric synthetic aperture radar (SAR) image data to measure ocean slopes and wave spectra. Independent techniques have been developed to measure wave slopes in the SAR azimuth and range directions. The azimuth slope technique, in particular, is a more direct measurement than conventional, intensity based, backscatter cross-section measurements.In the azimuth direction, wave-induced perturbations of the polarimetric orientation angle are used to sense the wave slopes. In the range direction, a new technique involving the alpha parameter from the Cloude-Pottier H-A-? (Entropy, Anisotropy, and (averaged) Alpha) polarimetric scattering decomposition theorem is used to measure slopes. Both measurement types are sensitive to ocean wave slopes and are directional. Taken together, they form a means of using polarimetric SAR (POLSAR) image data to make complete measurements of either ocean wave slopes, or directional wave spectra.These measurements must still contend with fundamental nonlinearities in the SAR image processing (i.e., azimuth direction “velocity bunching”) that are due to wave velocity and acceleration effects.NASA/JPL/AIRSAR L-, and P-band data from California coastal waters were used in the studies. Wave parameters measured using the new methods are compared with those developed using both conventional SAR intensity based methods, and with in situ NOAA National Data Center buoy measurement products.     

Improving SAR data processing with polarimetric reference functions in the range Doppler algorithm

Synthetic aperture radar (SAR) is a form of radar that can be used to create images of objects and landscapes. The main important application of the polarimetric SAR can be found in surface and target decomposition process of its image processing. In this article, we propose a method of polarimetric SAR data processing using two new polarimetric reference functions of canonical targets with the intention to apply in coherent decompositions. Our experiment uses polarimetric backscatter characteristics of the dihedral and trihedral reflectors as the targets under a ground-based SAR geometry to create the polarimetric reference functions for azimuth compression in the SAR data processing. We process the data using Pauli decomposition to investigate the effect of our functions on the RGB (red, green, and blue) properties of the processed images. The results show that Pauli decomposition using our functions produces images with different distribution and intensity of RGB colours in the image pixels with some signs of improvement over the traditional range Doppler algorithm. This demonstrates that our polarimetric reference function can be used in the decomposition steps of the traditional SAR data processing and can potentially be used to reveal some useful quantitative physical information of target points of interest and improve image and surface classification.     

SRTM DEM enhancement using a single set of PolSAR data based on the polarimetry-clinometry model

ABSTRACT

The freely available global and near-global digital elevation models (DEMs) have shown great potential for various remote sensing applications. The Shuttle Radar Topography Mission (SRTM) data sets provide the near-global DEM of the Earth’s surface obtained using the interferometry synthetic aperture radar (InSAR). Although free accessibility and generality are the advantages of these data sets, many applications require more detailed and accurate DEMs. In this paper, we proposed a modified and advanced polarimetry-clinometry algorithm for improving SRTM topography model which requires only one set of polarimetric synthetic aperture radar (PolSAR) data. The azimuth and range slope components estimation based on polarization orientation angle (POA) shifts and the intensity-based Lambertian model formed the bases of the proposed method. This method initially compensated for the polarimetry topography effect corresponding to SRTM using the DEM-derived POA. In the second step, using a modified algorithm, POA was obtained from the compensated PolSAR data. The POA shifts by the azimuth and range slopes’ variations based on the polarimetric model. In addition to the polarimetric model, a clinometry model based on the Lambertian scattering model related to the terrain slope was employed. Next, two unknown parameters, i.e. azimuth and range slope values, were estimated in a system of equations by two models from the compensated PolSAR data. Azimuth and range slopes of SRTM were enhanced by PolSAR-derived slopes. Finally, a weighted least-square grid adjustment (WLSG) method was proposed to integrate the enhanced slopes’ map and estimate enhanced heights. The National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA JPL) AIRSAR was utilized to illustrate the potential of the proposed method in SRTM enhancement. Also, the InSAR DEM was employed for evaluation experiments. Results showed that the accuracy of SRTM DEM is improved up to 2.91 m in comparison with InSAR DEM.     

A PolSAR ship detector based on a multi-polarimetric-feature combination using visual attention

Ship detection can be significantly improved by using polarimetric synthetic aperture radar (PolSAR) imaging. In this article, we propose a PolSAR ship detection method based on the use of multi-featured polarization by using the visual attention model. Three polarimetric features, namely, the polarimetric contrast, the polarimetric scattering, and the polarimetric phase, are selected as the early features, and the pros and cons for each feature are discussed. The visual attention model is a framework that rapidly combines multiple features into one feature, which is improved according to the relationship of the selected features. Validation of the method is performed by analysing the multi-resolution process, the improved multi-feature process, the threshold strategy, the sensibility to the incidence angle of the sensors, and the performance of moving ship detection, which are analysed by Radarsat-2 fine quad images with automatic identification system data. Additionally, the false alarm/non-detection analysis and the computation cost analysis are also considered. In contrast to other ship detectors, the proposed detector is more effective and robust.