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。

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.

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。

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。

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.

Ocean wave parameters retrieved directly from compact polarimetric SAR data

We aim to directly invert wave parameters by using the data of a compact polarimetric synthetic aperture radar(CP SAR)and validate the effectiveness of ocean wave parameter retrieval from the circular transmit/linear receive mode andπ/4 compact polarimetric mode.Relevant data from the RADARSAT-2 fully polarimetric SAR on the C-band were used to obtain the compact polarimetric SAR images,and a polarimetric SAR wave retrieval algorithm was used to verify the sea surface wave measurements.Using the data and algorithm,there is no need to estimate complex hydrodynamic modulation transfer functions,even at large radar incidence angles.First,the radar backscattering cross-sections and backscattering cross-section of the radar linearly polarized with any polarization orientation angle were calculated in the two compact polarimetric SAR modes.Then,the wave slopes along the azimuth direction and the range direction were calculated directly using CP SAR data.Finally,we obtained the slope spectrum of the wave from the estimated wave slopes along azimuth and range directions.The wave parameters extracted from the synthetic wave slope spectrum were compared with those obtained from buoy observations of the National Data Buoy Center,verifying a suitable agreement.

基于高分光学与全极化SAR的海南八门湾红树林种间分类方法

合理的红树种间组成结构是有效发挥红树林湿地生态价值的前提,明确的红树林种间分布信息是开展红树林生态系统治理和规划工作的有效依据。针对海南八门湾红树林湿地,基于高分三号(GF-3)全极化合成孔径雷达(synthetic aperture radar,SAR)和高分六号(GF-6)多光谱遥感数据,本文提取了35个红树林遥感特征,利用极端梯度提升树(eXtremegradientboosting,XGBoost)算法开展了特征重要性排序、特征筛选和红树林种间分类实验,将其与传统的支持向量机(supportvectormachine,SVM)、随机森林(randomforest,RF)机器学习算法进行精度比较,并基于XGBoost算法进行了3种特征组合方式(优选特征、多光谱特征、全极化SAR特征)的分类精度比较,旨在探索XGBoost对红树林种间分类的适用性和光学与全极化SAR数据对红树林种间分类的能力。结果表明:1)识别红树林种类的优势特征依次为多光谱的光谱波段、极化分解参数、光谱植被指数,且仅利用前8个优选特征(绿光波段反射率G、蓝光波段反射率B、Yamaguchi面散射分量Ys、近红外波段反射率NIR、增强型植被指数EVI、比值植被指数RVI、归一化植被指数NDVI、Freeman面散射分量F_(s))即可达到较高分类精度。2)对于八门湾红树林湿地,XGBoost算法的红树种间分类总体精度最高,为86.16%,卡帕系数为0.836,比SVM和RF算法高3%~8%;优选特征的红树林种间分类精度比单独的多光谱特征或全极化SAR特征高10%~12%。3)八门湾红树林总面积约为797.58hm^(2),共有白骨壤、海莲、红海榄、杯萼海桑、角果木、榄李、木榄、正红树、海漆9种优势真红树,杯萼海桑和木榄的面积较大,分别占全部红树林面积的45.46%、21.21%。

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.

基于超像素与稀疏重构显著性的极化SAR舰船检测

极化SAR舰船检测是极化SAR系统的重要应用之一,现有的极化SAR舰船检测方法在具有强背景杂波的条件下容易将强杂波误检为目标,造成虚警;在多尺度舰船检测情况下小尺寸的舰船容易淹没在背景杂波中,造成小尺寸目标的漏检.针对上述问题,本文提出一种基于超像素与稀疏重构显著性的极化SAR舰船检测方法.该方法首先用超像素分割方法将大幅的极化SAR场景图像分割,在超像素级别上使用稀疏重构显著性方法保留含有舰船目标的超像素,再在这些保留下来的超像素中逐像素使用稀疏重构显著性检测方法,得到最终的舰船检测结果.本文选取强杂波场景和多尺度舰船检测场景的两个场景的ALOS-2卫星极化SAR数据进行对比实验,实验结果表明,本文方法在强杂波场景下品质因数达到94.87%,在多尺度舰船检测场景下品质因数达到94.05%.

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.

一种改进的模糊Wishart-PSO极化SAR影像智能聚类算法

针对极化SAR影像聚类精度不高、极化参数数据量大、计算复杂的问题,本文提出了基于改进模糊Wishart距离的极化SAR影像粒子群智能聚类方法。该方法首先针对极化SAR数据分布,结合模糊划分改进传统Wishart聚类评价准则,减小孤立点噪声影响;然后根据极化散射机理完成聚类初始划分;最后在迭代寻优步骤引入粒子群优化框架,提高聚类中心有效性与分类精度。试验分别采用L波段AIRSAR数据及X波段高分辨率极化SAR数据验证了模糊Wishart-PSO聚类算法的有效性,分类结果较传统的H/α-Wishart方法合理性明显提高,聚类精度可达90%。

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.