A new maximum likelihood polarimetric interferometric synthetic aperture radar coherence change detection (ML-PolInSAR-CCD)

Coherent change detection (CCD) use the sample degree of coherence as a measure of the temporal change collected between two complex-valued SAR images observed in the same area and using also the same geometry and polarization. The problem of classical CCD approaches shows a temporal change only when there are in areas of the image that have high clutter-to-noise power ratio. All experiments regarding the maximum-likelihood (ML) CCD, found to estimate useful information also in lower clutter-to-noise power ratio. Experiments used only electromagnetic transmissions in the same polarization. This work extends the formulation of the probability distribution function of the CCD in a multi-dimensional version useful for multi-polarization SAR data. Results of this ML-polarimetric interferometric SAR-CCD (ML-PolInSAR-CCD) show surprising recovery of both amplitude and phase CCD information. This information recovery is useful for improved interferometric SAR (InSAR), permanent scatterers interferometry (PS-InSAR) and SAR tomography applications.     

Gini index-based land-cover classification using polarimetric synthetic aperture radar

Classification of the Earth's surface types is one of the important remote-sensing applications of radar polarimetry. An unsupervised classification scheme based on the use of entropy and alpha angle is widely used for land-cover classification using multi-polarization radar images. The polarimetric entropy and the alpha angle are used to characterize a target's randomness and scattering mechanism, respectively. Here, we replace the entropy by the Gini index. Evaluation of the Gini index is computationally efficient. It also overcomes the drawback encountered in entropy evaluation, namely, the use of logarithmic operation. We develop and validate an unsupervised classification technique based on the use of the Gini index and alpha angle and show that it performs better than the classic entropy/alpha classification technique. We have also used the Gini/alpha method with anisotropy and complex Wishart distribution to design a complete land-cover classification scheme. The proposed classification scheme performs better than the entropy/alpha land-cover classification scheme.     

基于对象的合成孔径雷达影像极化分解方法

面向对象方法已成为全极化合成孔径雷达（SAR）影像处理的常用方法,但是极化分解仍以组成对象的像素为计算单元,针对以像素为单位的极化分解效率低的问题,提出一种面向对象的极化分解方法。通过散射相似性系数加权迭代,获得对象的极化表征矩阵并对其收敛性进行了分析,以对象极化表征矩阵的极化分解代替对象区域内所有像素的分解,提高极化特征获取效率。在此基础上,综合影像对象空间特征,并通过特征选择与支持向量机(SVM)分类进行分析和评价。通过AIRSAR Flevoland影像数据实验表明,面向对象的分解方法能够减少对象极化特征提取的时间,同时提高地物目标的分类精度。相对于监督Wishart方法,提出方法的总体精度和Kappa值分别提高了17%和20%。     

基于四分量模型的极化SAR图像非监督分类

为有效地提高基于散射模型的非监督分类的分类精度,引入了Freeman三分量模型的改进模型-Yamaguchi四分量模型,并将该模型与威沙特距离模型结合起来.给出了基于四分量模型和威沙特距离的非监督分类、聚类算法及其实现流程.对AIRSAR数据集中的Flevoland图像选取了7个均匀程度不同的区域,进行了定性的、定量的实验,实验结果表明,新的分类、聚类算法能够显著的提高分类图的分辨率、更加清晰的表征地物的细节.该方法能够较大地提高均匀区域的分类精度.     

极化合成孔径雷达相干斑抑制极化信息保持能力定量评估

针对采用极化特征图主观评估PolSAR相干斑抑制算法的极化信息保持能力存在一定的不足,提出了一种基于极化特征图相关系数的相干斑抑制效果评估方法.该方法实现了对PolSAR相干斑抑制算法极化信息保持能力的定量评估,能够更为精确地反映不同滤波器及滤波参数变化对PolSAR散射特性的影响.仿真数据和实测ESAR数据的相干斑抑制效果评估实验,验证了该方法的有效性.     

SRAD-CNN for adaptive synthetic aperture radar image classification

The performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.     

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.     

Estimating boreal forest species type with airborne polarimetric synthetic aperture radar

We have applied a non-parametric classifier (k nearest neighbour) to two calibrated orthogonal passes of airborne polarimetric synthetic aperture radar (POLSAR) image data over boreal forest for the purpose of discriminating canopy tree species of predefined stands. We found that a single classifier based on a single feature space (i.e. one set of POLSAR variables for all species) was less accurate than a hierarchical two-stage classifier that used different POLSAR variables for each species. We designed a two-stage classifier that first grouped stands into broad classes: pine, spruce and deciduous, and then classified each sample within the broad classes into individual species. We found that the most effective feature spaces had two or three dimensions. The two-stage classifier attained overall accuracies of between 60% and 75%.

We provide a first use of an equivalency test applied to remote-sensing classification. We use Lloyd's test of equivalency to find equivalent classifiers and thus infer informative POLSAR variables. The POLSAR variables that were most informative varied between the two passes and between the various elements of the hierarchical classifier. For the initial three-class classifier the most informative POLSAR variables were the two circular polarization ratios, several of Touzi's Stokes vector variables, HHVV coherence, several texture measures such as the variance of several scattering coefficients and the order parameter of the K-distribution and characteristics of the polarization signature pedestal. These results demonstrate that C-band POLSAR has great potential for mapping boreal forest cover either on its own or in concert with other geospatial data.     

Urban land use classification using synthetic aperture radar

Abstract

This paper presents several approaches to the use of radar imagery for land use classification of urban and near-urban areas. The use of L(HH) (L band, horizontal transmit and horizontal receive) data is emphasized because it is these types of data obtained by Seasat-A (and in November 1981 by Shuttle radar) which are most generally available. For urban area studies using imaging radar the effect of processing in an off-zero doppler (‘squint’) mode, the presence of large diffuse scatters and the possibility of height measurements are discussed. Each approach provides information and also requires supporting ground truth which are unique to radar remote sensing. For some areas the coupling of data from the microwave portion of the spectrum to the data available in the visible and near visible realms may improve the classification of urban and near-urban land use. However, the radar data are not without their own limitations which may be imposed by either the system or the nature of the imaged scene. A proper knowledge of these limitations can permit us to turn a perceived defect into a decided advantage. The metropolitan area of Los Angeles provides the geographic background for this study.     

Decomposition of polarimetric synthetic aperture radar backscatter from upland and flooded forests

The goal of this research was to decompose polarimetric Synthetic Aperture Radar (SAR) imagery of upland and flooded forests into three backscatter types: single reflection, double reflection, and cross-polarized backscatter. We used a decomposition method that exploits the covariance matrix of backscatter terms. First we applied this method to SAR imagery of dihedral and trihedral corner reflectors positioned on a smooth, dry lake bed, and verified that it accurately isolated the different backscatter types. We then applied the method to decompose multi-frequency Jet Propulsion Laboratory (JPL) airborne SAR (AIRSAR) backscatter from upland and flooded forests to explain scattering components in SAR imagery from forested surfaces. For upland ponderosa pine forest in California, as SAR wavelength increased from C-band to P-band, scattering with an odd number of reflections decreased and scattering with an even number of reflections increased. There was no obvious trend with wavelength for cross-polarized scattering. For a bald cypress-tupelo floodplain forest in Georgia, scattering with an odd number of reflections dominated at C-band. Scattering power with an even number of reflections from the flooded forest was strong at L-band and strongest at P-band. Cross-polarized scattering may not be a major component of total backscatter at all three wavelengths. Various forest structural classes and land cover types were readily distinguishable in the imagery derived by the decomposition method. More importantly, the decomposition method provided a means of unraveling complex interactions between radar signals and vegetated surfaces in terms of scattering mechanisms from targets. The decomposed scattering components were additions to the traditional HH and V V backscatter. One cautionary note: the method was not well suited to targets with low backscatter and a low signal-to-noise ratio.     

Monitoring dewatering induced subsidence and fault reactivation using interferometric synthetic aperture radar

Using multitemporal differential interferometric synthetic aperture radar analysis integrated with pumping and site geologic data we present evidence for hydrologically induced large subsidence in and around an ongoing open‐pit mine with intensive dewatering operations. Analysis of numerous differential synthetic aperture radar interferometry (DInSAR) pairs spanning the period 1993 to 2001 reveals the abrupt appearance of these features to intervals of a few to several months. Along a section through the anomaly, we plotted dewatering associated changes in the groundwater levels at monitoring wells. We also used DInSAR to extract several individual kilometre‐lengths, centimetre amplitude normal fault reactivation events in the alluvial sediments adjacent to the mine dewatering operation. High‐resolution remote sensing analyses provide strong evidence that these features align with faults active in the last several thousand years. We interpret these reactivations as mechanically involving only the upper few hundred metres of the existing fault plane above the alluvial aquifer affected by the mine dewatering.     

A statistical distribution texton feature for synthetic aperture radar image classification

We propose a novel statistical distribution texton (s-texton) feature for synthetic aperture radar (SAR) image classification. Motivated by the traditional texton feature, the framework of texture analysis, and the importance of statistical distribution in SAR images, the s-texton feature is developed based on the idea that parameter estimation of the statistical distribution can replace the filtering operation in the traditional texture analysis of SAR images. In the process of extracting the s-texton feature, several strategies are adopted, including pre-processing, spatial gridding, parameter estimation, texton clustering, and histogram statistics. Experimental results on TerraSAR data demonstrate the effectiveness of the proposed s-texton feature.     

Multi-temporal monitoring of wetland water levels in the Florida Everglades using interferometric synthetic aperture radar (InSAR)

Interferometric synthetic aperture radar (InSAR) techniques can successfully detect phase variations related to the water level changes in wetlands and produce spatially detailed high-resolution maps of water level changes. Despite the vast details, the usefulness of the wetland InSAR observations is rather limited, because hydrologists and water resources managers need information on absolute water level values and not on relative water level changes. We present an InSAR technique called Small Temporal Baseline Subset (STBAS) for monitoring absolute water level time series using radar interferograms acquired successively over wetlands. The method uses stage (water level) observation for calibrating the relative InSAR observations and tying them to the stage's vertical datum. We tested the STBAS technique with two-year long Radarsat-1 data acquired during 2006-2008 over the Water Conservation Area 1 (WCA1) in the Everglades wetlands, south Florida (USA). The InSAR-derived water level data were calibrated using 13 stage stations located in the study area to generate 28 successive high spatial resolution maps (50 m pixel resolution) of absolute water levels. We evaluate the quality of the STBAS technique using a root mean square error (RMSE) criterion of the difference between InSAR observations and stage measurements. The average RMSE is 6.6 cm, which provides an uncertainty estimation of the STBAS technique to monitor absolute water levels. About half of the uncertainties are attributed to the accuracy of the InSAR technique to detect relative water levels. The other half reflects uncertainties derived from tying the relative levels to the stage stations' datum.     

Crop biomass estimation using multi regression analysis and neural networks from multitemporal L-band polarimetric synthetic aperture radar data

Biomass has a direct relationship with agricultural production and may help to predict crop yield. Earth observation technology can contribute significantly to monitoring given the availability of temporally frequent and high-resolution radar or optical satellite data. Polarimetric Synthetic Aperture Radar (PolSAR) has several advantages for operational monitoring given that at these longer wavelengths atmospheric and illumination conditions do not affect acquisitions and considering the sensitivity of microwaves to the structural properties of targets. Therefore, SARs are a promising source of data for crop mapping and monitoring. With increasing access to SARs the development of robust methods to monitor crop productivity is timely.

In this paper, we examine the use of machine learning and artificial intelligence approaches to analyze a time series of Polarimetric parameters for crop biomass estimation. In total, 14 polarimetric parameters from a time series of Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne L-band data were used for biomass estimation for an intensively cropped site in western Canada. Then, Multiple linear regression (MR) and artificial neural network (ANN) models were developed and evaluated to estimate the biomass for canola, corn, and soybeans. According to the experimental results, the ANN provided more accurate biomass estimates compared to MR.

Canola biomass, in general, showed less sensibility to almost all the polarimetric parameters. Nevertheless, Freeman-Double combined with vertical-vertical backscattering (VV) delivered the correlation coefficient (r) of 0.72, and the root mean square error (RMSE) of 56.55 g m^?2of canola biomass. For corn, the highest correlation was observed between a pairing of horizontal- horizontal backscattering (HH) with Entropy (H) for biomass estimation yielding an r of 0.92 and RMSE of 196.71 g m^?2. Horizontal-vertical backscattering (HV) and Yamaguchi-Surface (O_Y) delivered the highest sensitivity for soybeans (r of 0.82 and RMSE of 13.48 g m^?2). If all crops are pooled, H combined with O_Y provided the most accurate estimates of biomass (r of 0.89 and RMSE of 135.31 g m^?2). These results demonstrated that models which make use of polarimetric parameters that characterize the multiple sources of scattering typical of vegetation canopies can be used to estimate crop biomass accurately. Such results bode well for agricultural monitoring considering the increasing number of satellite SAR sensors with various frequencies, imaging modes and revisit times. As such, the time series analysis and methods proposed in this study could be used to monitor crop development and productivity using SAR space technologies.     

Observation of atmospheric effects on repeat-pass interferometric synthetic aperture radar over the Kuwait desert

Twenty-eight advanced synthetic aperture radar (ASAR) scenes from the Environmental Satellite (ENVISAT) are analysed to select suitable pairs for generating a digital elevation model (DEM) and displacement maps. For this purpose, the repeat-pass interferometric synthetic aperture radar (InSAR) technique is implemented using GAMMA interferometric modules. The perpendicular component of baseline (B┴) is taken as the criteria for selecting the pairs: 0 < B┴ <100 m for displacement maps and 200 < B┴ < 400 m for the DEM. Though there are many pairs satisfying the above criteria, only four case studies are presented here to illustrate the effects of atmosphere on the DEM and displacement maps over the Kuwait desert climate. In each case study, two examples are selected: one where the atmosphere is a serious problem and another example the atmosphere has no significant problem. The DEM of the Shuttle Radar Topographic Mission (SRTM) is taken as a reference for root mean square (RMS) error estimation in the DEM. The RMS error varies from as low as 2 m to as high as 40 m. Some DEMs showed fringe-like structures resembling precipitable water vapour (PWV) fields. Similarly, the measured displacement values were found to vary randomly from place to place and time to time. The displacement maps showed vertical structures similar to PWV. The DEM was corrected for PWV. The results are encouraging. From this study, it is clear that, even for desert areas, there is a need to look into the effects of PWV on the DEM and displacement maps before the results are used.     

基于多层支持向量机的极化合成孔径雷达特征分析与分类

为了充分利用极化合成孔径雷达（SAR）图像不同极化特征对不同地物目标类型的刻画能力,提出一种基于多层支持向量机（SVM）的极化SAR特征分析与分类方法。该方法首先通过特征分析确定适合不同地物类型的最佳特征子集;然后采用分层分类树的方式,根据每一种地物类型的特征子集逐层进行SVM分类;最终得到整体分类结果。RadarSAT-2极化SAR图像分类实验结果表明所提方法水域、耕地、林地、城区4类地物分类精度为85%左右,总体分类精度达到86%。该算法充分利用了不同地物目标类型的特性,提高了分类精度,也降低了算法时间复杂度。     

全极化合成孔径雷达影像地形纠正及其在雪冰制图中的应用

针对合成孔径雷达（SAR）影像由于地形起伏引起的图像畸变问题,文章提出了基于相干矩阵的全极化SAR影像地形纠正算法,并运用于雪冰制图。该方法首先采用距离多普勒模型建立SAR成像几何模型;然后利用全极化Cloude特征分解方法对全极化SAR图像进行融合,将融合后的SAR图像与模拟图像进行配准提高SAR影像几何定位精度;最后利用投影面积归一化和极化方位角移动补偿技术对地形引起的辐射畸变进行纠正。采用中国长江源区南部唐古拉山中段冬克玛底冰川区域的C波段Radarsat-2全极化SAR数据进行验证,配准模拟SAR和原始SAR影像的控制点方位向和距离向的均方根误差（RMSE）分别为7.765和14.586个像素;经过地形纠正后的地物分类精度达80%以上。结果表明：（1）该方法能够有效消除SAR影像中几何和辐射畸变的影响;（2）地形纠正后的SAR数据在雪冰制图中具有可行性。     

单极化合成孔径雷达图像颜色特征编码与分类

针对目前单极化合成孔径雷达（SAR）伪彩色编码方法存在的细节信息和可视性不强的问题,提出一种颜色特征编码方法。该颜色特征编码方法首先对单极化SAR图像提取纹理特征;然后将每一个特征量化到0到255;其次对每一个灰度级赋予一个RGB颜色,编码成颜色特征图;最后对随机森林计算得到的特征重要性进行排序,每3维特征对应为R、G、B通道生成伪彩图。基于该颜色特征编码方法,提出一种新的分类方法。该分类方法首先根据目视效果选择可分性最好的伪彩图;然后采用统计区域合并（SRM）分割算法对其分割;其次将所有RGB伪彩图作为分类的特征,以随机森林为分类器进行分类,得到初步的结果;最后对初步的结果进行相对多数投票,得到最终的分类结果。方法验证采用两组TerraSAR-X单极化SAR数据,与基于HIS的颜色编码方法对比,该颜色特征编码方法生成的伪彩图信息熵得到了很大提升,且两组数据每类地物的分类精度都大幅度提高,因此证明了所提算法保留了更多的细节信息,获取更多的颜色信息,更利于可视化和地物分类,从而表明提出的颜色特征编码方法是可行的。     

Unsupervised classification using polarimetric interferometric decomposition and Wishart classifier

ABSTRACT

In this paper, a decomposition scheme of the coherency matrix is presented to parse the information of polarimetric interferometric synthetic aperture radar (PolInSAR) images in detail. First, the decomposition method is improved by the polarimetric interferometric similarity parameter (PISP) to relief the overestimation occurred in the traditional four-component decomposition method. Second, after using the improved four-component decomposition results as the original inputs, the decomposition method is applied to retrieve scattering mechanisms or identify scatters, with the image separated into seven subsections. Finally, based on the modified decomposition results, the basic classification results are regarded as the feature training sets, and the Wishart classifier is then used as an optimized classification process. The applications of the decomposition and classification scheme are shown with typical representative L-band E-SAR images, which are used to show the robustness of the method, as well as with the first published airborne C-band PolInSAR data collected by the Institute of Electronics, Chinese Academy of Sciences, in November 2017. Experimental results demonstrate that the obtained decomposition and classification results are in good agreement with the actual physical scattering mechanisms.     

Imaging algorithm for synthetic aperture interferometric radiometer in near field

With the benefits of digital IC technology development, the synthetic aperture interferometric radiometer (SAIR) technique is growing fast and expanding to more and more application areas. The near field imaging detection is a potential application which has received increasing demand recently. Because the Fourier imaging theory of the traditional SAIR is based on far-field approximation, it will be invalid for near-field condition. This paper is devoted to establishing a new accurate imaging algorithm for ...