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.     

极化方位角对Yamaguchi参数分解的影响

以极化方位角估计为基础,对由方位向地形引起的极化分解误差进行深入探讨,详细分析方位向地形对4参数Yamaguchi分解的影响,同时利用全极化SAR方位角估计减少地形因素带来的影响。最后,利用AIRSAR系统于1992年在San Francisco地区的全极化数据进行实验分析。实验结果表明,利用极化方位角补偿的方法,可以改善Yamaguchi分解的精度。     

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.     

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.     

Terrain analysis using radar shape-from-shading

This paper develops a maximum a posteriori (MAP) probability estimation framework for shape-from-shading (SFS) from synthetic aperture radar (SAR) images. The aim is to use this method to reconstruct surface topography from a single radar image of relatively complex terrain. Our MAP framework makes explicit how the recovery of local surface orientation depends on the whereabouts of terrain edge features and the available radar reflectance information. To apply the resulting process to real world radar data, we require probabilistic models for the appearance of terrain features and the relationship between the orientation of surface normals and the radar reflectance. We show that the SAR data can be modeled using a Rayleigh-Bessel distribution and use this distribution to develop a maximum likelihood algorithm for detecting and labeling terrain edge features. Moreover, we show how robust statistics can be used to estimate the characteristic parameters of this distribution. We also develop an empirical model for the SAR reflectance function. Using the reflectance model, we perform Lambertian correction so that a conventional SFS algorithm can be applied to the radar data. The initial surface normal direction is constrained to point in the direction of the nearest ridge or ravine feature. Each surface normal must fall within a conical envelope whose axis is in the direction of the radar illuminant. The extent of the envelope depends on the corrected radar reflectance and the variance of the radar signal statistics. We explore various ways of smoothing the field of surface normals using robust statistics. Finally, we show how to reconstruct the terrain surface from the smoothed field of surface normal vectors. The proposed algorithm is applied to various SAR data sets containing relatively complex terrain structure.     

极化散射与SAR 遥感信息获取

合成孔径雷达(SAR ) 对地观测与成像技术是近20 年来空间微波遥感技术最重要的进展。JPL 的SIR-C SAR 与加拿大Radarsat SAR-2 等星载或机载SAR 的全极化散射测量提出了自然地表全极化散射信息获取与处理的关键性科学问题。充分理解自然地表极化散射特性, 进而发展自然地表特征信息的分类、识别和反演算法是SAR 遥感应用的关键问题。近年来, 对于极化SAR 遥感已有广泛的研究。自然地表全极化散射的数值建模与M ueller 矩阵模拟解、相干矩阵及其特征值分析、信息熵等都有了研究与应用。但是, 如何将SAR 图像相干矩阵特征值和信息熵的全极化散射测量与自然地表特征参数直接关联, 并由此发展地表的分类、识别与参数反演等信息获取与处理还有待于大量的研究。本文将论述我们在SAR 全极化散射理论与应用的若干研究进展。第一个问题是如何将SAR 图像相干矩阵特征值和信息熵与同极化、交叉极化后向散射系数的测量直接关联, 与M ueller 矩阵解一起研究地表的分类与识别。第二个问题是如何有全极化散射测量反演地面数字程(DEM )。第三个问题是如何利用多时相SAR 遥感识别、获取与评估地面特征时间上的变化。     

A simple method to account for topography in the radiometric correction of radar imagery

This article presents a method to study and correct radiometric distortions caused by topography in SAR images. The method is easy to implement and requires neither sophisticated software nor code-level programming. It also considers the case of a flat surface having an elevation different from the one for which calibration parameters were derived. An ortho-image of the slant range distance is used with a Digital Elevation Model to generate images of the local incident angle along the range and azimuth directions. The method compensates for variations in the terrain area of each pixel and for the angular dependence of backscatter, allowing the choice of either an empirical or semi-empirical scattering model. The method is applied to high-resolution C-SAR subsets of an agricultural area in the Central Cordillera of Costa Rica. The removal of topographic features appears excellent for local incident angles up to 80°, but small-scale structures have pronounced effects on the radar return for higher local incident angles and are not adequately corrected.     

山区地形星载SAR影像的几何纠正

随着搭载合成孔径雷达的各种卫星不断发射,SAR的研究越来越受到重视。由于SAR数据独特的成像方式,山区地形的星载SAR图像几何形变十分复杂。通常应用控制点,采用多项式拟合的方法已经无法将其改正。依据SAR的几何成像模型,利用有关卫星轨道参数和数字高程模型,进行山区地形SAR影像的几何纠正研究。研究利用少量轨道参数和DEM数据,通过坐标变换和投影成像误差纠正建立正确的坐标位置,并采用邻近元采样法完成几何纠正。以上方法应用于山区ERS-1/SAR影像的处理试验结果表明,该方法能够用于山区复杂地形的几何纠正,其误差小于2个像元。     

利用雷达摄影测量方法提取DEM及其精度评价

为了解雷达立体摄影测量中各项因素对最终DEM精度的影响,采用马来西亚热带雨林地区具有不同波束模式和入射角的6对Radarsat-1影像,应用两种不同的SAR成像模型,即距离/多普勒模型和等效共线方程模型,对使用雷达摄影测量方法提取DEM进行了试验.首先分析了两种成像模型不同的物理基础,然后通过比较由它们得到的DEM的精度,发现利用距离/多普勒模型提取DEM的精度优于等效共线方程模型;然后,利用研究区的数字地形图等参考资料,分析了不同轨道、不同模式、不同分辨率、不同交角的立体像对组合以及地形因素对DEM精度的影响,要得到高精度的DEM,必须综合考虑立体像对的选取和研究区的地形、地貌等特征.     

Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution

Multi-look polarimetric SAR (synthetic aperture radar) data can be represented either in Mueller matrix form or in complex covariance matrix form. The latter has a complex Wishart distribution. A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution. This algorithm can also be applied to multifrequency multi-look polarimetric SAR data, as well as 10 SAR data containing only intensity information. A procedure is then developed for unsupervised classification.

The classification error is assessed by using Monte Carlo simulation of multilook polarimetric SAR data, owing to the lack of ground truth for each pixel. Comparisons of classification errors using the training sets and single-look data are also made. Applications of this algorithm are demonstrated with NASA/JPL P-, L- and C-band polarimetric SAR data.     

Fuzzy classification of earth terrain covers using complex polarimetric SAR data

The conventional approach of terrain image classification that assigns a specific class for each pixel is inadequate, because the area covered by each pixel may embrace more than a single class. Fuzzy set theory which has been developed to deal with imprecise information can be incorporate in the analysis for a more appropriate solution to this problem. In the current state of imaging radar technology, polarimetric synthetic aperture radar (SAR) is unique in providing complete polarization information of ground covers for more effective classification than a single polarization radar. In this paper, we use the fuzzy c-means clustering algorithm for unsupervised segmentation of multi-look polarimetric SAR images. A statistical distance measure adopted in this algorithm is derived from the complex Wishart distribution of the complex covariance matrix. In classifying polarimetric SAR imagery, each terrain class is characterized by its own feature covariance matrix. The algorithm searches for cluster centres for each class and generates a fuzzy partition for the whole image. Membership grades obtained for each pixel provide detailed information about spatial terrain variations. Classification of the image is achieved by choosing a defuzzification criterion. When the back-scattering characteristics of two or more classes are not well distinguished from each other, a divisive hierarchical clustering procedure is adopted to locate their respective feature covariance matrices. NASA/JPL AIRSAR data is used to substantiate this fuzzy classification algorithm.     

深度置信网络在极化SAR图像分类中的应用

目的 深度置信网络能够从数据中自动学习、提取特征,在特征学习方面具有突出优势。极化SAR图像分类中存在海量特征利用率低、特征选取主观性强的问题。为了解决这一问题,提出一种基于深度置信网络的极化SAR图像分类方法。方法 首先进行海量分类特征提取,获得极化类、辐射类、空间类和子孔径类四类特征构成的特征集;然后在特征集基础上选取样本并构建特征矢量,用以输入到深度置信网络模型之中;最后利用深度置信网络的方法对海量分类特征进行逐层学习抽象,获得有效的分类特征进行分类。结果 采用AIRSAR数据进行实验,分类结果精度达到91.06%。通过与经典Wishart监督分类、逻辑回归分类方法对比,表现了深度置信网络方法在特征学习方面的突出优势,验证了方法的适用性。结论 针对极化SAR图像海量特征的选取与利用,提出了一种新的分类方法,为极化SAR图像分类提供了一种新思路,为深度置信网络获得更广泛地应用进行有益的探索和尝试。     

An algorithm for estimating the noise levels in polarimetric SAR image data

A novel algorithm is used to estimate the noise level in polarimetric SAR image data channels, by using the measured signature of an idealized surface with Bragg Scattering. This estimated noise level can then be used to correct the measured backscatter signatures from polarimetricSAR image data.     

雷达遥感的地质学应用及其进展

合成孔径雷达(SAR)遥感以其独有的全天时、全天候观测能力和对地表的穿透性及形态探测能力,特别是现在新型成像雷达技术的发展,使之在地质学应用中具有独特的优势。结合SAR应用技术的发展阶段,即由单波段单极化到多波段多极化,再发展到现在极化测量和干涉测量阶段,综述了成像雷达遥感在地学中的应用,特别是对新型成像雷达技术(极化雷达、干涉雷达)的地学应用作了介绍。     

The identification of sea swell using polarimetric radars: A new full wave approach

The statistical characteristics and the orientation of a remotely generated swell superimposed upon a local wind generated rough sea surface are determined from computer simulated polarimetric data. The full wave approach is used to determine the incoherent backscatter Mueller matrix elements and the incoherent backscatter Graves power matrix elements as functions of azimuth angle, the colatitude (angle measured from the vertical axis), and rough sea surface (with swell) parameters. The relationships between the incoherent backscatter Mueller matrix as well as the incoherent backscatter Graves power matrix and the statistical characteristics of the swell and its orientation are presented here. On making suitable selections of the backscatter angle and polarization of the Mueller matrix elements or Graves power matrix elements, it is possible to determine reliably the orientation and statistical characteristics of remotely generated swells. Traditionally only the scattering cross sections are utilized in remote sensing.     

Automated delineation of coastline from polarimetric SAR imagery

In this paper we present a new diffusion-based method for the delineation of coastlines from space-borne polarimetric SAR imagery of coastal urban areas. Both polarimetric filtering and speckle reducing anisotropic diffusion (SRAD) are exploited to generate a base image where speckle is reduced and edges are enhanced. The primary edge information is then derived from the base image using the instantaneous coefficient of variation edge detector. Next, the resulting edge image is parsed by a watershed transform, which partitions the image into disjoint segments where the division lines between segments are collocated with detected edges. The over-segmentation problem associated with the watershed transform is solved by a region merging technique that combines neighbouring segments with similar radar brightness. As a result, undesired boundary segments are eliminated and true coastlines are correctly delineated. The proposed algorithm has been applied to a space-borne polarimetric SAR dataset, demonstrating a good visual match between the detected coastline and the manually contoured coastline. The performance of the proposed algorithm is compared with those of two polarimetric SAR classification algorithms and two edge-based shoreline detection methods that are tailored to single polarization SAR images. Experimental results are shown using polarimetric SAR data from Hong Kong.     

基于物理散射模型的全极化SAR图像增强滤波算法

针对传统的极化SAR滤波方法图像中城镇区域和植被区域地物在滤波中易被混淆, 导致滤波后图像中地物边缘保持效果下降的问题, 提出了一种增强的保持极化散射特性的滤波算法。利用一种增强的四分量极化分解方法获取更加精确的地物散射机制, 并将散射机制信息引入滤波方法中, 使滤波算法中像素的散射机制更精确。增强的四分量极化分解方法引入了极化SAR数据的定向角补偿技术、一种新的体散射模型以及两种散射功率限制条件, 来改进Freeman-Durden分解的结果。理论分析和实验结果表明, 改进后的方法获取了比传统的极化SAR图像滤波算法更加理想的计算结果。     

基于Yamaguchi分解模型的全极化SAR图像分类

针对利用Yamaguchi分解模型的四个散射分量直接进行类别归属判断精度不高并且所分类别有限的问题,结合模糊C均值的理论,提出了一种基于Yamaguchi分解模型的全极化SAR分类算法,把四个散射分量组成一组归一化的特征矢量,进行FCM聚类分析。并且用日本机载L波段PiSAR数据验证了该算法具有较高的分类精度和较好的视觉效果。     

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.