Shallow sea topography detection using fully Polarimetric Gaofen-3 SAR data based on swell patterns

Compared to single-polarization synthetic aperture radar(SAR)data,fully polarimetric SAR data can provide more detailed information of the sea surface,which is important for applications such as shallow sea topography detection.The Gaofen-3 satellite provides abundant polarimetric SAR data for ocean research.In this paper,a shallow sea topography detection method was proposed based on fully polarimetric Gaofen-3 SAR data.This method considers swell patterns and only requires SAR data and little prior knowledge of the water depth to detect shallow sea topography.Wave tracking was performed based on preprocessed fully polarimetric SAR data,and the water depth was then calculated considering the wave parameters and the linear dispersion relationships.In this paper,four study areas were selected for experiments,and the experimental results indicated that the polarimetric scattering parameterαhad higher detection accuracy than quad-polarization images.The mean relative errors were 14.52%,10.30%,12.56%,and 12.90%,respectively,in the four study areas.In addition,this paper also analyzed the detection ability of this model for different topographies,and the experiments revealed that the topography could be well recognized when the topography gradient is small,the topography gradient direction is close to the wave propagation direction,and the isobath line is regular.

An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

We estimate tree heights using polarimetric interferometric synthetic aperture radar(PolInSAR)data constructed by the dual-polarization(dual-pol)SAR data and random volume over the ground(RVoG)model.Considering the Sentinel-1 SAR dual-pol(SVV,vertically transmitted and vertically received and SVH,vertically transmitted and horizontally received)configuration,one notes that S_(HH),the horizontally transmitted and horizontally received scattering element,is unavailable.The S_(HH)data were constructed using the SVH data,and polarimetric SAR(PolSAR)data were obtained.The proposed approach was first verified in simulation with satisfactory results.It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest,North Carolina,USA.According to local observations and forest descriptions,the range of estimated tree heights was overall reasonable.Comparing the heights with the ICESat-2 tree heights at 23 sampling locations,relative errors of 5 points were within±30%.Errors of 8 points ranged from 30%to 40%,but errors of the remaining 10 points were>40%.The results should be encouraged as error reduction is possible.For instance,the construction of PolSAR data should not be limited to using SVH,and a combination of SVH and SVV should be explored.Also,an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

基于DRCNN的PolSAR图像分类综合实验设计

为了让学生更好地了解和掌握深度学习TensorFlow框架和CNN网络,采用基于不同区域的多尺度卷积神经网络(DRCNN)设计了PolSAR图像分类综合设计实验,旨在实现遥感图像的自动化分类和理解。极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)图像能够提供更加丰富的极化信息,更好地刻画地物特征,对国防建设和国家发展具有重要意义。实验利用Python语言,在CNN基础上进行改进研究,设计了多区域的多尺度CNN模型,实现了极化SAR图像的数据处理、特征学习和分类一体化设计。该实验不仅可以帮助学生综合应用图像处理与深度学习知识,理解和利用CNN来进行极化SAR图像分类的基本原理和方法,还能使学生更加深入、熟练地掌握TensorFlow框架,提高学生的科研素质和动手实践能力。

Information compression and speckle reduction for multifrequency polarimetric SAR images based on kernel PCA

Multifrequency polarimetric SAR imagery provides a very convenient approach for signal processing and acquisition of radar image. However, the amount of information is scattered in several images, and redundancies exist between different bands and polarizations. Similar to signal-polarimetric SAR image, multifrequency polarimetric SAR image is corrupted with speckle noise at the same time. A method of information compression and speckle reduction for multifrequency polarimetric SAR imagery is presented based on kernel principal component analysis （KPCA）. KPCA is a nonlinear generalization of the linear principal component analysis using the kernel trick. The NASA/JPL polarimetric SAR imagery of P, L, and C bands quadpolarizations is used for illustration. The experimental results show that KPCA has better capability in information compression and speckle reduction as compared with linear PCA.

Targets detecting in the ocean using the cross-polarized channels of fully polarimetric SAR data

Azimuth ambiguities （ghost targets） discrimination is of great interest with the development of a synthet- ic aperture radar （SAR）. And the azimuth ambiguities are often mistaken as actual targets and cause false alarms. For actual targets, HV channel signals acquired by a fully polarimetric SAR are approximately equal to a VH channel in magnitude and phase, i.e., the reciprocity theorem applies, but shifted in phase about ±π for the first-order azimuth ambiguities. Exploiting this physical behavior, the real part of the product of the two cross-polarized channels, i.e. （SHVSVH）, hereafter called A12r, is employed as a new parameter for a target detection at sea. Compared with other parameters, the contrast of A12r image between a target and the surrounding sea surface will be obviously increased when A12r image is processed by mean filtering algo- rithm. Here, in order to detect target with constant false-alarm rates （CFARs）, an analytical expression for the probability density function （pdf） ofA12r is derived based on the complexWishart-distribution. Because a value of A12r is greater/less than 0 for real target/its azimuth ambiguities, the first-order azimuth ambiguities can be completely removed by this A12r-based CFAR technology. Experiments accomplished over C-band RADARSAT-2 fully polarimetric imageries confirm the validity.

STUDY ON SPECKLE REDUCTION IN MULTI-LOOK POLARIMETRIC SAR IMAGE

This paper studies the speckle reduction in multi-look polarimetric synthetic aperture radar (SAR) image. A multi-look polarimetric whitening filtering (MPWF) method is presented and extended to form a fully polarimetric filter with multi-channel output. The paper also quantifies the speckle reduction amount achievable by the MPWF, and compares the MPWF with the span, weighting and power equalization methods. Experimental results with the NASA/JPL L-band 4-look polarimetric SAR data verify the effectiveness and superiority of the MPWF, and show that the MPWF is of great advantage for enhancing SAR image classification.

CLASSIFICATION OF MULTI-LOOK POLARIMETRIC SAR IMAGERY AND POLARIZATION CHANNEL OPTIMIZATION

In this paper, a new maximum likelihood (ML) classification algorithm is proposed to classify the multi-look polarimetric synthetic aperture radar (SAR) imagery. Experimental results with the NASA/JPL airborne L-band polarimetric SAR data demonstrate the effectiveness of the new algorithm. Furthermore, when using the algorithm in the classifications with subsets of the multi-look polarimetric SAR data, the polarization-channel optimization for the terrain type classification is implemented.

Analysis of impacting factors on polarimetric SAR oil spill detection

Polarimetric synthetic aperture radar （SAR） oil spill detection parameters conformity coefficient （μ）, Muller matrix parameters （｜C｜,B0 ）, the eigenvalues of simplified coherency matrix （λnos） and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena （oil slick and look likes）. The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches （low wind and biogenic slick） with the signal below the noise, polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes （low wind and biogenic slick） and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polafimetric SAR data oil spill detection should be carried out on the basis of noise consideration.

AN UNSUPERVISED CLASSIFICATION FOR FULLY POLARIMETRIC SAR DATA USING SPAN/H/α IHSL TRANSFORM AND THE FCM ALGORITHM

In this paper, the IHSL transform and the Fuzzy C-Means (FCM) segmentation algorithm are combined together to perform the unsupervised classification for fully polarimetric Synthetic Ap-erture Rader (SAR) data. We apply the IHSL colour transform to H/α/SPANspace to obtain a new space (RGB colour space) which has a uniform distinguishability among inner parameters and contains the whole polarimetric information in H/α/SPAN.Then the FCM algorithm is applied to this RGB space to finish the classification procedure. The main advantages of this method are that the parameters in the color space have similar interclass distinguishability, thus it can achieve a high performance in the pixel based segmentation algorithm, and since we can treat the parameters in the same way, the segmentation procedure can be simplified. The experiments show that it can provide an improved classification result compared with the method which uses the H/α/SPANspace di-rectly during the segmentation procedure.

Integration of SAR Polarimetric Features and Multi-spectral Data for Object-Based Land Cover Classification

An object-based approach is proposed for land cover classification using optimal polarimetric parameters.The ability to identify targets is effectively enhanced by the integration of SAR and optical images.The innovation of the presented method can be summarized in the following two main points:①estimating polarimetric parameters(H-A-Alpha decomposition)through the optical image as a driver;②a multi-resolution segmentation based on the optical image only is deployed to refine classification results.The proposed method is verified by using Sentinel-1/2 datasets over the Bakersfield area,California.The results are compared against those from pixel-based SVM classification using the ground truth from the National Land Cover Database(NLCD).A detailed accuracy assessment complied with seven classes shows that the proposed method outperforms the conventional approach by around 10%,with an overall accuracy of 92.6%over regions with rich texture.

Structural adaptive and optimal speckle filtering in multilook full polarimetric SAR images

A novel approach is proposed for speckle reduction in multilook full polarimetric SAR images. In contrast to others, this approach adopts an enhanced structure detection method to estimate the parameters of the polarimetric covariance matrix for the multilook polarimetric whitening filtering （MPWF） algorithm and thus a structural adaptive and optimal speckle filter is developed. To evaluate the present approach, NASA SIR-C/X- SAR, L band, four-look processed polarimetric SAR data of the Tian-Mountain Forest is used for simulation. Experimental results demonstrate the effectiveness of this novel filtering algorithm in case of both speckle reduction and preservation of texture information. Comparisons with other methods are also made.

STATISTICAL ANALYSIS OF MULTI-LOOK POLARIMETRIC SAR IMAGERY

With a multiplicative speckle model, this paper shows the multi-look polarimetric synthetic aperture radar (SAR) data obeys a generalized K-distribution. To validate this distribution model, the multi-look intensity K-distribution is particularly tested. The relationship between the heterogeneity coefficient of the scene and the proper statistical model is experimentally established. In addition, based on the results of the statistical analysis, an adaptive classification scheme is presented, and the improved classification shows the importance of the statistical analysis.

Supervised polarimetric SAR classification method based on Fisher linear discriminant

A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant. The feature parameters used in this classification method could be se- lected flexibly according to land covers to be classified. Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be com- bined together for classification use, without consideration of the dimension difference of each fea- ture parameter and the joint probability density function of those parameters. Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94. 33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data, demonstrating the effectiveness of this method.

A method for coastal oil tank detection in polarimetric SAR images based on recognition of T-shaped harbor

To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.

ADAPTIVE MODEL-BASED SCATTERING DECOMPOSITION OF POLARIMETRIC SAR INTERFEROMETRY

In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.

Analysis of a Residual Model for the Decomposition of Polarimetric SAR Data

Accurate estimation of the double-bounce scattering fd and surface scattering fs coefficients with Freeman-Durden decomposition is still difficult. This difficulty arises because overestimation of the volume scattering energy contribution Pv leads to negative values for fd and fs. A generalized residual model is introduced to estimate fd and fs. The relationship between Pv and the residual model is analyzed. Eigenvalues computed from the residual model must be positive to explain physical scattering mechanisms. The authors employ a new volumetric scattering model to minimize Pv as calculated by several decomposition methods. It is concluded that decreasing Pv can help reduce negative energy. This conclusion is validated using actual polarimetric SAR data.

基于融合距离的极化SAR图像非局部均值滤波

在极化合成孔径雷达(synthetic aperture radar,SAR)图像降噪领域,常见的非局部均值滤波仅依靠像素间的统计距离进行相似性度量,忽略了像素点的空间信息。本文结合极化SAR数据统计特性和图像空间特征作为像素间的相似性度量,提出了一种利用融合距离来计算相邻窗口权重的方法——基于融合距离的非局部均值滤波器。融合距离的引入使得滤波器能够更全面的评估像素间的相似性,从而得到更合适的像素权重。此外,本方法还引进变异系数对邻域窗口的权重进行评估,通过该参数可以控制滤波的程度。在多幅极化SAR图像上的实验结果表明,所提出的滤波器能够在有效抑制斑点噪声的同时保留较为完整的图像边缘信息和极化散射特性。

微波视觉三维SAR实验系统及其全极化数据处理方法

三维合成孔径雷达在测绘制图、防灾减灾等诸多领域有应用潜力,已经成为SAR的重要研究方向。为减少三维SAR的观测次数或天线阵元数量,推动三维SAR的应用和发展,中国科学院空天信息创新研究院牵头研制了微波视觉三维SAR实验系统,旨在为微波视觉SAR三维成像提供实验平台和数据。该文针对微波视觉三维SAR实验系统及其全极化数据处理方法进行介绍,涵盖了极化校正、极化相干增强、极化约束三维成像、三维融合可视化等全流程的关键步骤。基于发布的SAR微波视觉三维成像全极化数据集,给出了三维成像结果示例,验证了微波视觉三维SAR实验系统的全极化性能以及处理方法的有效性。该文发布的数据集将为SAR三维成像研究提供良好的数据条件。

陆探一号卫星SAR载荷关键技术

陆探一号是我国首个采用差分干涉测量技术实现地表形变监测应用的合成孔径雷达(SAR)卫星星座,其01组双星分别于2022年1月26日和2022年2月27日成功发射并在轨运行。双星在轨后具有双基编队干涉和双星组网两种模式。在双星干涉成像模式下,利用双基InSAR条带模式获取全国高精度DEM;在双星组网成像模式下,双星同轨道间隔180°在轨稳定运行,重访周期由8 d下降至4 d,获取时序相干数据,具备高精度地表形变监测的能力。此外,陆探一号SAR系统具备多模式极化优势,可获取单航过多极化InSAR、混合极化SAR数据,可用于林业、国土资源调查及灾害监测等。本文对陆探一号SAR载荷的关键技术,包括相位同步、模糊抑制与系统定标等进行系统性阐述和分析。陆探一号SAR载荷实现的最高分辨率为3 m,最大幅宽可达400 km,同时干涉模式方位模糊度优于-20 dB,系统性能优良,本文通过地面测试和在轨实测数据对陆探一号多模式成像性能进行验证。

基于双极化SAR数据的深圳市地表形变研究

旨在挖掘数据的极化信息,利用覆盖深圳市填海地区的Sentinel-1A卫星IW模式升轨影像,进行基于双极化SAR数据提取地表形变的相对优化极化组合选择实验,以PSInSAR技术处理影像,获取填海区域地表形变速率,分析重点沉降区域时序形变特征,结合SBAS技术处理结果进行对比验证。实验表明,使用VV+VH极化组合方式的SAR数据在地表形变提取的精度与范围上均优于VV极化方式的SAR数据;发现研究区域主要有三个形变漏斗,分布在宝安区福州大道附近、蛇口水湾路附近、福田区益田地铁站附近。最大沉降速率-16.7~-14.26 mm/a,集中在福田区南部;最大抬升速率为7.6~10.4 mm/a,集中在蛇口区域。