A quad polarimetric SAR calibration algorithm using rotation symmetry

Polarimetric calibration, precisely deciphering the polarimetric information hidden in the polarized characteristics of a radar scene, is the core step before applying quad polarimetric synthetic aperture radar (PolSAR) data for ground parameter inversion and classification. The previously published techniques for polarimetric calibration generally use at least one known point calibration target to completely determine the polarimetric distortions (cross-talks and channel imbalances) between the various polarized channels. This paper describes a novel method that solely relies on the image itself with the property of rotation symmetry. The algorithm derives the entire cross-talk and channel imbalance parameters using an iterative operation to circularly modify the observed average covariance matrix, which is independent on the known point calibration targets in the illuminated scene. The proposed method is validated to be reliable and efficient through polarimetric calibration experiments using the airborne C-band and RadarSat-2 quad polarimetric SAR images. The experimental results indicate that the new technique achieves similar calibration precision to the Ainsworth algorithm but without using any known calibration point target.     

Stacked auto-encoder for classification of polarimetric SAR images based on scattering energy

This paper proposes a new algorithm, for polarimetric synthetic aperture radar (PolSAR) classification, based on a stacked auto-encoder and scattering energy. Previous approaches to PolSAR classification predominantly consider only the single pixel of distribution of the polarimetric data and scattering characteristics, and ignore other kinds of image features like the relationship of the local pixels. Besides, because of the complexities of PolSAR data, it is difficult to compute the derivatives that are needed for back-propagation in deep-learning classifiers. To overcome these difficulties, we propose a new approach that combines the scattering power and stacks sparse auto-encoder (Scattering SSAE) for PolSAR classification. Firstly, orientation compensation is used to compensate the polarization orientation angle, reducing the impact of polarimetric angle noise. Secondly, Freeman-Durden decomposition is adopted to extract three basic scattering powers: surface, double bounce and volume. Each PolSAR image pixel is transformed into these scattering powers, yielding a new kind of feature from the PolSAR data. Finally, using the three kinds of scattering power as inputs, we combine local spatial information using a patch-based approach, and use a deep learning architecture to achieve classification. We compare our method against several other state-of-the-art methods using ground-truthed test-data, and show that the Scattering SSAE method achieves higher accuracy than other methods on most categories.     

基于张量空间中的均值漂移聚类的极化SAR图像分割

提出了一种基于均值漂移(Mean Shift, MS)聚类的全极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)图像无监督分割算法. 已有的工作在将MS算法应用于全PolSAR图像分割时, 仅使用每个像素点的极化总功率值作为该像素点的特征值, 没有充分利用极化协方差矩阵或者相干矩阵所包含的完整的极化散射信息. 但是如果直接利用每个像素点的极化协方差矩阵作为特征向量, 则这些特征向量构成的空间不再是一个欧氏空间, 而原始的MS算法是定义在欧氏空间中的. 因此, 本文首先将每一个像素点的厄尔米特正定极化协方差矩阵也称为一个张量, 而且使用黎曼流形来描述该张量空间. 然后, 原始的MS算法被扩展到该张量空间中. 直接扩展得到的算法每一步具有明确的含义, 但是运算复杂度较高. 所以本文又进一步对该算法进行了简化, 从而得到了一个实用的分割算法. 通过使用真实的全PolSAR数据以及仿真数据进行实验, 结果验证了新方法的有效性.     

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.     

A novel super-resolution method of PolSAR images based on target decomposition and polarimetric spatial correlation

The polarimetric synthetic aperture radar (PolSAR) is becoming more and more popular in remote-sensing research areas. However, due to system limitations, such as bandwidth of the signal and the physical dimension of antennas, the resolution of PolSAR images cannot be compared with those of optical remote-sensing images. Super-resolution processing of PolSAR images is usually desired for PolSAR image applications, such as image interpretation and target detection. Usually, in a PolSAR image, each resolution contains several different scattering mechanisms. If these mechanisms can be allocated to different parts within one resolution cell, details of the images can be enhanced, which that means the resolution of the images is improved. In this article, a novel super-resolution algorithm for PolSAR images is proposed, in which polarimetric target decomposition and polarimetric spatial correlation are both taken into consideration. The super-resolution method, based on polarimetric spatial correlation (SRPSC), can make full use of the polarimetric spatial correlation to allocate different scattering mechanisms of PolSAR images. The advantage of SRPSC is that the phase information can be preserved in the processed PolSAR images. The proposed methods are demonstrated with the German Aerospace Center (DLR) Experimental SAR (E-SAR) L-band full polarized images of the Oberpfaffenhofen Test Site Area in Germany, obtained on 30 September 2000. The experimental results of the SRPSC confirms the effectiveness of the proposed methods.¹     

PolSAR image compression based on online sparse K-SVD dictionary learning

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.     

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.     

Similarity-enhanced target detection algorithm using polarimetric SAR images

Target detection and analysis using polarimetric synthetic aperture radar (PolSAR) images are currently of great interest in synthetic aperture radar (SAR) applications. For a complex target, the scattering characteristics are determined by different independent sub-scatterers and their interaction; therefore, the scattering characteristics should be described by a statistical method due to randomness and depolarization. Furthermore, the inherent speckle in SAR data must be reduced by spatial averaging at the expense of loss of spatial resolution. The polarimetric similarity parameter (PSP) is an effective parameter to analyse target characteristics. In order to describe a complex distributed target, two new methods for calculating PSP are proposed, namely Stokes matrix-based PSP (S-PSP) and multiple PolSAR similarity parameter (MPSP). The characteristics of a target can be described and extracted on the basis of the polarimetric similarity, and then the similarity-enhanced target detection methods using S-PSP and MPSP are implemented and demonstrated with German Aerospace Centre (DLR) experimental SAR L-band multiple temporal PolSAR images of Oberpfaffenhofen test site (DE), Germany. The results confirmed that the proposed methods are effective for detection and analysis of buildings in urban areas.     

高分辨率极化合成孔径雷达成像研究进展

合成孔径雷达（Synthetic aperture radar,SAR）是一种有效的地球遥感技术,对观测区域进行全天时、全天候的高分辨率大范围成像,在军事侦察、环境监测和地质测绘等领域有着十分广泛的应用。随着雷达技术和地球科学的发展,人们期望能够获取更多的目标特性,传统的单极化SAR已经难以满足越来越多元化的实际应用需求。极化合成孔径雷达（Polarimetric synthetic aperture radar,PolSAR）基于多个极化通道获取目标不同极化状态下的散射特性丰富了SAR图像的信息量,拓展了SAR的应用领域。从极化数据中准确地解译目标的物理特性是PolSAR应用的重要前提。本文对PolSAR的研究进展进行了总结,重点介绍了极化目标分解算法,给出了高分辨率PolSAR实测数据处理结果,并对未来研究方向进行了展望。     

Crop discrimination based on polarimetric correlation coefficients optimization for PolSAR data

Crop discrimination is a necessary step for most agricultural monitoring systems. Radar polarimetric responses from various crops strongly relate to the types and orientations of the local scatterers, which makes the discrimination still difficult using the polarimetric synthetic aperture radar (PolSAR) technique. This work provides a new approach by investigating and utilizing the characteristics of polarimetric correlation coefficients in the rotation domain along the radar line of sight. The theoretical basis lies in that polarimetric correlation coefficients can reflect the different responses and can be enhanced at different levels for various land-cover types with suitable rotation angles in the rotation domain. In this vein, a polarimetric correlation coefficient optimization framework is established and new polarimetric features are extracted therein. Demonstration with multi-frequency (P-, L-, and C-bands) airborne synthetic aperture radar (AIRSAR) PolSAR data over crop areas validates that polarimetric correlation coefficients are crop dependent and the optimized polarimetric correlation coefficient parameters can better discriminate them. Then, a crop discrimination scheme is proposed using the derived polarimetric features. A flow chart for the optimal discrimination feature set selection and determination is provided and is validated by the real data with seven typical crop types. All these crop types are successfully discriminated for the P- and L-band data, whereas only two types of crops are slightly overlapped in the feature space for the C-band data. Experimental studies demonstrate the efficiency and potential of the established methodology.     

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.     

Unsupervised classification for polarimetric SAR images based on the improved CFSFDP algorithm

In polarimetric synthetic aperture radar (PolSAR) image processing, the number of classes is an important factor for PolSAR image classification. Therefore, how to accurately estimate the number of PolSAR image classes is an important issue. In this article, we propose a novel unsupervised classification method which can accurately estimate the number of classes for PolSAR images. First, the PolSAR image is initialized into many small clusters by using the complementary information from Yamaguchi decomposition and distribution characteristics of data. Second, the improved clustering by fast search and find of density peaks, named as improved CFSFDP algorithm, is introduced to select the appropriate category number. Finally, to improve the representation of each category, the PolSAR data set is classified by an iterative fine-tuning process based on a complex K-Wishart function. The performance of the proposed classification approach is presented and analysed on three real data sets. The experimental results show that the proposed classification method can accurately estimate the category number and enhance the classification accuracy in comparison with other traditional methods. It is also shown that the data distribution characteristic has the additional information beyond the target scattering decomposition, and this information is important for the initialization.     

Enhanced edge detection for polarimetric SAR images using a directional span-driven adaptive window

ABSTRACT

Automatic edge detection for polarimetric synthetic aperture radar (PolSAR) images plays a fundamental role in various PolSAR applications. The classic methods apply the fixed-shape windows to detect the edges, whereas their performance is limited in heterogeneous areas. This article presents an enhanced edge detection method for PolSAR data based on the directional span-driven adaptive (DSDA) window. The DSDA window has variable sizes and flexible shapes, and is constructed by adaptively selecting samples that follow the same statistical distribution. Therefore, it can overcome the limitation of classic fixed-shape windows. To obtain refined and reliable edge detection results in heterogeneous urban areas, we adopt the spherically invariant random vector (SIRV) product model since the complex Wishart distribution is often not met. In addition, a span ratio is combined with the SIRV distance to highlight the dissimilarity measure and to improve the robustness of the proposed method. The simulated PolSAR data and three real data sets from experimental synthetic aperture radar, electromagnetics institute synthetic aperture radar, and Radarsat-2 systems are used to validate the performance of the enhanced edge detector. Both quantitative evaluation and visual presentation of the results demonstrate the effectiveness of the proposed method and its superiority over the classic edge detectors.     

Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints

Spectral clustering is a very popular approach which has been successfully used in unsupervised classification of polarimetric synthetic aperture radar (PolSAR) imagery. However, due to its high computational complexity, spectral clustering can only be applied to small data sets. This article provides a framework for spectral clustering of large-scale PolSAR data. As computing and processing the pairwise-based affinity matrix is the bottleneck of the spectral clustering approach, we first introduce a representative points-based scheme in which a memory-saving and computationally tractable affinity matrix is designed. The subsequent spectral analysis can be solved efficiently. Second, a simple one-parameter superpixel algorithm is introduced to generate representative points. Through these superpixels, spatial constraints are also naturally integrated into the classification framework. We test the proposed approach on both airborne and space-borne PolSAR images. Experimental results demonstrate its effectiveness.     

PolSAR anisotropic diffusion filter with a refined similarity measure and an adaptive fidelity constraint

The presence of speckle complicates the tasks of interpreting and analysing polarimetric synthetic aperture radar (PolSAR) images. The nonlinear anisotropic diffusion (AD) method has been found to perform well in removing image noise. In this article, we propose a new AD method for PolSAR speckle reduction. An iterative refinement method is employed to measure the similarity between pixels and construct the diffusion coefficient in the iteration process, by considering both information in the original speckled image and the restored image of the last iteration. In addition, to alleviate the over-smoothing problem the conventional AD methods often encounter, an adaptive fidelity constraint is added into the diffusion equation by considering local heterogeneity information and the amount of noise. Experiments on both simulated and real PolSAR images confirm the ability of the proposed method to both suppress speckle and retain image details.     

基于监督非相干字典学习的极化SAR图像舰船目标检测

提出了一种结构化非相干字典学习算法 (Structured incoherent dictionary learning, SIDL),并将该方法应用于极化SAR (Polarimetric synthetic aperture radar, PoLSAR)图像舰船目标检测. 在字典学习阶段,构建了一个新的目标函数,为了降低子字典对交叉样本的稀疏表示能力, 将子字典对交叉样本的重构能量约束及子字典互相干性约束加入到字典学习目标函数中. 通过这两个约束, 降低了子字典对交叉样本的表示能力,目标和杂波的极化特征矢量在学习获得的字典下具有良好的区分特性. 该方法不依赖于目标后向散射能量,只利用学习获得的极化字典,根据测试样本在极化字典下的稀疏表示进行目标的检测. 实验采用RADARSAT-2数据进行了验证,对比实验结果表明,本文提出的方法可以更好地抑制杂波,对弱小目标实现检测,获得了更好的检测效果.     

结合编码-解码网络和条件随机场的全极化合成孔径雷达土地覆盖分类

针对多极化合成孔径雷达影像地物分类特征表征性较弱及全卷积网络分类精度较低的问题,文中提出结合编码-解码网络(E-D-Net)和条件随机场(CRF)的全极化合成孔径雷达(SAR)土地覆盖分类算法.首先,利用Freeman分解和Pauli分解建模全极化SAR影像,提取各分解对应的散射特征.再借鉴语义分割网络模型的建模思想和多尺度卷积单元构建对称网络模型,将多尺度非对称卷积单元嵌入中层,设计E-D-Net网络模型.通过E-D-Net网络模型对PolSAR影像Freeman分解散射特征进行多层自主学习,获得初始分类结果.最后,利用全连接CRF结合Pauli相干分解伪彩色图信息,对初始分类结果再进行降噪和平滑优化,得到最终分类结果.在两地区PolSAR影像上的实验验证文中算法的有效性和可行性.     

一种新的基于Wishart MRF的全极化SAR图像分类方法

无监督的Wishart分类算法在多次迭代后,容易出现错分现象,即多个类别属于同一类散射机制,或者多种散射都拥有相同的类别标签。针对此问题,提出了一种新的基于Wishart MRF的无监督全极化SAR图像分类方法。新方法改进了散射机制保持的方式,即并不是完全限制像素点的散射机制,而是根据像素点的散射机制在迭代过程中给定一个有限的范围。同时,使用一种自适应区域的MRF方法来提取像素点的先验信息。该方法不仅考虑了全极化SAR数据的散射性质,而且结合了统计特性和邻域信息,并在一定程度上保持了散射性质。实验结果证明,与传统的Wishart和基于散射机制保持的Wishart算法相比,该方法在JPL/NASA的AIRSAR数据上取得了更好的分类结果。     

高分辨率极化SAR图像水平集分割

目的 传统的极化SAR图像分割方法中,由于采用的统计分布模型不能较好地描述高分辨率的图像纹理特征,导致高分辨率极化SAR图像分割效果较差。针对这个问题,本文将具有广泛适用性的KummerU分布嵌入到水平集极化SAR图像分割方法中,提出了一种新的极化SAR图像分割算法。方法 将KummerU分布作为高分辨率极化SAR图像的统计模型,定义一种适用于极化SAR图像分割的能量泛函;利用最大似然法对各个区域的KummerU分布进行参数估计,并通过数值偏微分方程的方法求解水平集函数,实现极化SAR图像的区域分割。结果 分别对仿真全极化数据,真实全极化数据进行分割实验,结果表明本文提出的方法其分割精度高于传统方法,分割精度高于95%,从而验证了新方法的有效性。结论 本文算法能够对各向同质区和各向异质区的极化SAR图像都能取得良好的分割效果,并适应于多种场景,有效地分割出背景和目标。     

极化SAR相干斑抑制的非局部加权最小均方误差滤波算法

目的 相干斑的存在严重影响了极化合成孔径雷达(PolSAR)的影像质量.对相干斑的抑制是使用SAR数据的必不可少的预处理程序.提出一种基于非局部加权的线性最小均方误差(LMMSE)滤波器的极化SAR滤波的方法.方法 该方法的主要过程是利用非局部均值的理论来获取LMMSE估计器中像素样本的权重.同时,在样本像素的选取过程中,利用待处理像素的极化散射特性和邻域块的异质性来排除不相似像素以加速算法,同时达到保持点目标和自适应调节块窗口大小的目的.结果 模拟影像和真实影像上进行的实验结果表明,采用这种方法滤波后影像的质量得到明显改善.和传统的LMMSE算法相比,无论是单视的影像还是多视的影像,本文方法去噪结果的等效视数都高出8视以上;峰值信噪比也提升了5.8 dB.同时,去噪后影像分类的总体精度也达到了83％以上,该方法的运行效率也比非局部均值算法有了较大提升.结论 本文方法不仅能够有效抑制相干斑噪声,还能较好地保持边缘和细节信息以及极化散射特性.这将会为后续高效利用SAR数据提供保障.