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。     

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.     

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.     

Weighted Wishart distance learning for PolSAR image classification

An approach of weighted Wishart distance learning, shorted for W2-based distance learning, is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. It aims to adjust the Wishart distance by enhancing discrimination as well as exploiting spatial information. The proposed distance learning keeps samples within the same category close and separates samples from the different classes far apart. It is effectively implemented by solving a linear programming. Input of W2-based distance learning is called weighted Wishart feature, which is designed specifically for PolSAR data to describe the Wishart distribution, achieve regional consistency, and reduce speckle noise. Weight is calculated according to an adaptive window, where homogeneous samples are derived based on a connected region and extracted edge information. With this feature, W2-based distance learning is a whole scheme to adjust the Wishart distance. Furthermore, our experiments with benchmark data sets suggest that the proposed scheme provides both improved performance in terms of visual effect and classification accuracy. The achieved overall accuracy is better by more than 7% compared to other state-of-art methods.     

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     

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.     

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.     

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 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.     

A PolSAR image speckle filtering method preserving point targets and dominant scattering mechanisms

ABSTRACT

A new approach in polarimetric synthetic aperture radar (PolSAR) speckle filtering is proposed in this article. The proposed method preserves both point targets and dominant scattering mechanisms. The point targets are detected based on the span image, and they are then neither filtered nor involved in the other pixels’ filtering. To achieve the protection of the dominant scattering mechanism of each pixel, only pixels of the same dominant scattering mechanism as the centre pixel are included in the selection of the homogeneous pixels. Both point targets not being filtered and fact that only pixels of the same dominant scattering mechanism are included in the selection of the homogeneous pixels, which greatly improves the filtering efficiency. A likelihood-ratio test statistic based on the PolSAR covariance matrices is applied to determine the homogeneous pixels. Finally, the speckle filtering is processed using the weighted minimum mean square error estimator on the homogeneous pixels. We demonstrate the obvious advantages of the proposed method over other algorithms in the preservation of point targets and dominant scattering mechanisms, speckle suppression, protection of detail information, and maintenance of polarization information, by the use of both simulated and real PolSAR data.     

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.     

LBP特征分类的极化SAR图像机场跑道检测

目的 在极化合成孔径雷达（synthetic aperture radar，SAR）图像中常用直线检测进行机场跑道的识别，但是河流、道路等与机场跑道具有相似直线的地物容易对检测结果造成干扰，出现检测目标难定位、目标模糊、多虚警等问题。为此，本文设计了一种利用目标散射特性结合局部二值模式（local binary patterns，LBP）特征分类的极化SAR图像机场跑道区域检测方法，采用LBP特征对极化SAR图像进行有监督的分类来提取真实的机场区域。方法 首先利用异化散射功率对极化SAR图像进行阈值分割，然后通过形态学处理得到疑似机场跑道区域，同时构建机场跑道和非机场跑道两类训练样本，并提取、统计样本的LBP特征，形成直方图，得到特征向量训练支持向量机（support vector machine，SVM）二分类器，其中SVM二分类器采用了径向基函数（radial basis function，RBF）核函数；接着对疑似机场跑道区域构建LBP特征，送入SVM二分类器中分类，对机场跑道进行检测识别，最终得到真实的机场跑道区域。结果 利用UAVSAR（uninhabited aerial vehicle synthetic aperture radar）系统采集的7幅极化SAR图像数据进行实验检测，并选取基于几何特征辨识跑道的两种算法进行对比，3种方法均有效检测出了7幅场景中的真实跑道，但是本文方法在7幅数据中总的虚警和漏警个数均为1，而两种对比算法中的虚警个数分别为2和11、漏警个数分别为8和1。结论 本文方法不仅能有效检测出机场跑道区域，且检测效果更好，计算量较小，虚警和漏警率低，效率更高。     

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.     

Improved airborne PolSAR calibration algorithm based on time-variant attitude compensation

The polarimetric synthetic aperture radar (PolSAR) usually has to be calibrated before practical application, so as to compensate for polarimetric distortion. The varying platform attitude is one of the factors causing distortion but has rarely been considered in existing polarimetric calibration algorithms. With the resolution of PolSAR systems improving and the synthetic aperture time prolonging, this factor cannot simply be ignored. The varying attitude will distort the polarimetric information by rotating the polarimetric orientation angle, and such distortion changes with azimuth time. In this article, we modified the conventional polarimetric system model to take account of the time-variant impact of the unstable platform attitude. A calibration algorithm is proposed to compensate the time-variant attitude impact on the raw return data. The proposed calibration algorithm is tested on the data collected by Institute of Electronics, Chinese Academy of Sciences P-band PolSAR system. Results show that it can achieve better performance by reducing crosstalk error than two conventional methods.     

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.     

基于深度学习和层次语义模型的极化SAR分类

针对复杂场景的极化合成孔径雷达（Synthetic aperture radar,SAR）图像,堆叠自编码模型能够自动学习高层特性,有效表示城区、森林等复杂地物的结构,然而,却难以保持图像的边界和细节.为了克服该缺点,本文结合深度自编码器和极化层次语义模型（Polarimetric hierarchical semantic model,PHSM）,提出了新的无监督的极化SAR图像分类算法.该方法根据极化层次语义模型,将复杂的极化SAR图像划分为聚集、匀质和结构三大区域.对聚集区域,采用堆叠自编码模型进行高层特征表示,并构造字典得到稀疏特征进行分类;对匀质区域,采用层次模型进行分类;对于结构区域,进行线目标保留和边界定位.实验结果表明,该算法通过不同的分类策略优势互补,能够得到区域一致性好且边界保持的分类结果.     

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.     

Change detection using distance-based algorithms between synthetic aperture radar polarimetric decompositions

The present study introduces distance based change detection (CD) algorithms in polarimetric synthetic aperture radar (PolSAR) data. PolSAR images, due to interactions between electromagnetic waves and target and because of the high spatial resolution, can be used to study changes in the Earth’s surface. The purpose of this paper is to use features extracted from the fully-polarimetry imaging radar that involved Yamaguchi four-component and H/A/α decomposition based on the distance between the vectors of features for CD. We first extract features from polarimetric decompositions of multi-looked covariance (or coherency) matrix data. We then use two well-known distance measures namely Canberra and Euclidean distances for measuring the similarity between the vectors of polarimetric decompositions at different times. Assessment of incorporated methods is performed using different criteria, such as overall accuracy, area under the receiver operating characteristic curve, and false alarms rate. The results of the experiments show that Canberra distance has better performance with high overall accuracy and low false alarm rate than Euclidean distance and other compared algorithms to detect changes.     

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.