InSAR技术获取高山峡谷区DEM精度研究

通过对金沙江河段高山峡谷区L波段的Alos-Palasar和C波段的Radarsat-2雷达单视复数数据的干涉处理,获取此区域的数字高程模型(DEM)。利用SRTM 90m分辨率的DEM为参考数据,通过对比分析发现InSAR技术生成的DEM精度与相干系数、地形和波长有密切的关系。同时也验证了在相干性好,地形起伏不太剧烈的地区,用InSAR技术生成DEM是可行的。     

星载SAR干涉技术获取DEM及其精度分析

星载合成孔径雷达干涉（InSAR）技术是一种数据覆盖范围广、廉价、高效、方便的数字高程模型（DEM）获取方法,但在地面植被覆盖广、大气水汽含量高的地区其影像相干性随时间基线的增加迅速降低;同时,SAR卫星的轨道误差也影响DEM精度。利用ERS-1/2卫星串行模式SAR数据获取镇江地区DEM,分析了轨道误差对DEM精度的影响;根据干涉相位的统计特性,从理论上给出干涉相位噪声与相干系数和视数之间的关系。实验结果表明就干涉像对的卫星轨道误差和相位噪声而言,在小区域内DEM精度优于3.5 m。     

基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究

针对不同成像机理的光学与雷达遥感数据协同应用于地表信息提取瓶颈问题，提出了一种基于地形信息的光学与雷达数据协同分类方法。首先利用InSAR测量技术从Radarsat-2数据中提取DEM地形信息，然后构建基于地形信息的Landsat光学数据和Radarsat-2雷达数据的不同特征集输入模型，最后通过随机样本选取构建随机森林（Random Forest,RF）、支持向量机（Support Vector Machine, SVM）和决策树（Decision Tree,DT）分类算法模型提取地表信息。结果表明：①针对不同特征协同策略，在随机选取10%训练样本时，Radarsat-2干涉提取DEM与Landsat数据集提取精度优于ASTER GDEM与光学影像协同策略；②针对不同地表信息提取算法模型，通过50次随机选取训练样本构建模型评价分类精度，验证RF算法的鲁棒性和提取精度都要优于DT算法和SVM算法。研究充分利用光学和雷达遥感的优势信息，为光学和雷达遥感协同地表信息提取提供新的思路。     

基于地形信息的Landsat与Radarsat-2遥感数据协同分类研究

针对不同成像机理的光学与雷达遥感数据协同应用于地表信息提取瓶颈问题,提出了一种基于地形信息的光学与雷达数据协同分类方法。首先利用InSAR测量技术从Radarsat-2数据中提取DEM地形信息,然后构建基于地形信息的Landsat光学数据和Radarsat-2雷达数据的不同特征集输入模型,最后通过随机样本选取构建随机森林（Random Forest,RF）、支持向量机（Support Vector Machine, SVM）和决策树（Decision Tree,DT）分类算法模型提取地表信息。结果表明：①针对不同特征协同策略,在随机选取10%训练样本时,Radarsat-2干涉提取DEM与Landsat数据集提取精度优于ASTER GDEM与光学影像协同策略;②针对不同地表信息提取算法模型,通过50次随机选取训练样本构建模型评价分类精度,验证RF算法的鲁棒性和提取精度都要优于DT算法和SVM算法。研究充分利用光学和雷达遥感的优势信息,为光学和雷达遥感协同地表信息提取提供新的思路。     

利用星载InSAR技术提取镇江地区DEM及其精度分析

InSAR技术是目前获取高精度数字高程模型（DEM）的一种新方法。为了分析InSAR技术提取DEM的精度,首先介绍了美国航天飞机雷达SRTM DEM的精度和数据结构,然后以江苏镇江地区作为试验区,采用ERS1/2卫星影像来提取DEM,并对星载SAR提取的DEM与SRTM 3弧秒分辨率DEM的精度作了比较。 结果表明,利用星载SAR提取的DEM分辨率与SRTM 3弧秒分辨率的DEM相当,能很好地显示出地形起伏（如山脉、沟谷）的纹理特征。进一步的研究还表明,利用InSAR技术提取DEM的精度与SRTM 3 DEM之间存在5米左右的系统误差,并对产生这一系统误差的原因作了详细分析。     

Efficient mitigation of atmospheric phase effects in repeat-pass InSAR measurements

The problem of atmospheric phase effects is currently one of the most important limiting factors for widespread application of repeat-pass interferometric synthetic aperture radar (InSAR) measurements. Due to the extraordinary complexity of the atmospheric inhomogeneity and turbulence, it is generally difficult to obtain satisfactory mitigation of the atmospheric phase effects in repeat-pass InSAR measurements. In recent years, several methods have been developed for mitigating the atmospheric phase effects. An effective approach is interferogram stacking, which is based on stacking independent interferograms. However, as many as 2n images are required to generate n interferograms and the atmospheric delay errors of the stacked interferogram decrease only with the square root of the number of interferograms in the conventional interferogram stacking method, which is not very efficient. In order to efficiently mitigate the atmospheric phase effects on the stacked interferogram in repeat-pass InSAR measurements, we propose a relay-interferogram stacking method. Compared with the conventional method, this method not only can efficiently mitigate atmospheric phase effects on the stacked interferogram, but also greatly decreases the number of required synthetic aperture radar (SAR) images. The key element is that the first and the last SAR images are selected from the periods of similar meteorological conditions. In addition, we present an application of the approach to the study of ground subsidence in the area around Beijing, China.     

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.     

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.     

A comparison of TerraSAR-X, RADARSAT-2 and ALOS-PALSAR interferometry for monitoring permafrost environments, case study from Herschel Island, Canada

Interferometric synthetic aperture radar (InSAR) data sets from TerraSAR-X, RADARSAT-2 and ALOS-PALSAR are compared for their ability to detect ground movement over the continuous permafrost site of Herschel Island, Yukon Territory, Canada. All three sensors maintain good coherence within a summer season and can be used to create summer displacement products. Stacking is advantageous for the TerraSAR-X and RADARSAT-2 data sets, although mottling, possibly an interaction of the SAR with vegetation, or residual tropospheric noise, is visible, reducing the reliability of the results. RADARSAT-2 and ALOS-PALSAR provide the most promising results with the ability to form one year interval interferograms. PALSAR can also form two and three year interval interferograms. Long interval data sets spanning 2007 to 2010 identify a band of movement of 20 to 30 cm/year along the north-east coast, and a region of movement of up to 5 cm/year near the northern tip of the island. The ability to form long interval displacement products holds the most promise for permafrost monitoring, since long-term trends are of greater interest for permafrost stability than short-term seasonal changes. TerraSAR-X data have the disadvantage that year to year interferograms cannot be formed. InSAR is not the ideal monitoring technique for the large thaw slumps of Herschel Island. Although general areas of instability can be identified, specific slump detection is limited by radar look direction, and the large and abrupt slump movement, often accompanied by disintegration and collapse of slump sections, causes loss of coherence in the InSAR data. Thaw slumps may require a different interferometric approach, such as slump extent mapping from coherence loss, or the installation of corner reflectors and point target techniques. The frequent revisit and high spatial resolution of TerraSAR-X provide the best chance of maintaining coherence over thaw slumps. In general, InSAR is more successful at identifying broad areas of subtle subsidence in gentle relief, areas of terrain instability, possibly due to permafrost thaw or ground ice melt and the removal of water volume, and prior to significant slumping.     

多模式雷达在矿区沉降监测中的应用研究

合成孔径雷达差分干涉测量(DInSAR)技术在地表形变监测方面已得到广泛应用。介绍了将差分InSAR技术运用于矿区地表沉降监测,获得了河北峰峰煤矿地表Envisat/ASAR和ALOS/PALSAR的雷达形变干涉相位图,并对Envisat C波段和ALOS L波段的形变干涉相位图进行了相干特性和相位特性的分析。通过综合考虑C波段和L波段的优势与不足,将两者联合使用,实验表明利用多模式雷达数据对矿区地表沉降进行检测的可行性。同时,通过对雷达干涉相位图的分析,能够及时提供正在进行地下开采活动的矿区地理位置。     

Detection of river/sea ice deformation using satellite interferometry: limits and potential

In this paper, we present a study consisting of the application of radar interferometry for river/sea ice monitoring in inhabited regions and on commercial waterways. The sites studied are located in Canadian regions where ice jams constitute a common winter hazard that can cause extensive socio-economic damage and impose severe restrictions on ship traffic. ERS and Radarsat images were jointly used with traditional in situ observations to detect ice break-up in order to prevent ice jams and related problems. A coherence study served to define the synthetic aperture radar interferometry (InSAR) limits for river/sea ice dynamics monitoring. Other factors that also help to define the limits of InSAR technology for this application include the frequency of image acquisition, the minimum dimension of detected ice floes and the determination of appropriate ice types. Significant phase shifts were found for small ice floes of several hundred metres with ERS-tandem images. The analysis of the interferograms showed that it is possible to detect deformations in the ice shelf and to discriminate quantitatively the horizontal and vertical components of ice movement when the interferograms are combined with traditional observations such as meteorological data, water level, water flow and ice charts. The deformation estimated on a piece of fast river ice can be interpreted as the first sign of the ice break-up. On an estuary river that is a busy seaway, a qualitative interpretation of the interferograms served to highlight the interaction of river and tidal flows affecting the ice cover. We showed, in particular, the potential of radar interferometry and its integration with other techniques to help the authorities to prevent problems related to ice jams.     

Analysis of ground subsidence at a coal-mining area in Huainan using time-series InSAR

In Huainan City, Anhui Province, China, ground subsidence persistently occurs due to underground coal mining, which has caused several environmental issues. A modified time-series Interfermetric synthetic aperture radar (InSAR) technique is applied to obtain ground movement in Huainan over a period of approximately two years using 20 ascending Radarsat-2 images. In particular, distributed scatterers (DSs) are efficiently identified using classification information and statistical characteristics. Classified information is used to identify the specific DS classes and statistical characteristics are used to refine the DS candidates. To control error propagation and improve computational efficiency, the deformation rate and digital elevation model (DEM) error of persistent scatterers (PSs) are first retrieved using conventional persistent scatterer intermemetry (PSI). Then, a region-growing-based strategy is applied to extract the deformation rate of DSs. Land subsidence is detected in coal-mining areas of central Dingji, Guqiao, Zhangjiaji, Xinji I, and Xinji II, which suggests that the subsidence is primarily caused by underground mining. The fastest subsidence occurring in non-urban areas is particularly vulnerable to subsidence and collapsed lakes. It is also found that the collapsed lakes have expanded during the observation period, with an expanded mining area of 0.842 km² near Guqiao. A linear relationship between the observed subsidence and expansion of collapsed lakes was found, which indicates that the expansion of collapsed lakes hass resulted from subsidence due to underground coal mining.     

高分辨率SAR正射影像制作精度控制

以SAR正射产品生产的流程工艺为序,从精度控制的角度出发,着重对影响SAR影像正射产品精度的相关因素进行理论分析,并以同一地区不同入射角的5景Radarsat-1 SAR影像为实例,结合实地GPS采集的140个控制点给出了生产高精度SAR影像正射产品的相关实验结论和生产建议。     

Advanced InSAR techniques for deformation studies and for simulating the PS-assisted calibration procedure of Sentinel-1 data: case study from Thessaloniki (Greece), based on the Envisat/ASAR archive

Using state-of-the-art InSAR techniques, namely persistent scatterers (PSs) and small baseline subset (SBAS) approaches, this study contributes to open geotechnical questions in the area of Thessaloniki (Greece) from a remote-sensing perspective. It also demonstrates the potential of these techniques for calibration purposes, with reference to the new C-band synthetic aperture radar (SAR) sensor on board the Sentinel-1 mission satellites. By exploiting the historical archive of Envisat/ASAR data, as well as a pair of the first Sentinel-1A SAR images, recent (2004–2010) deformation rates up to 18 mm year^–1 are detected over the study area. These results are then compared to the findings of previous InSAR and geophysical observations, indicating for example, subsidence or tectonic activity. On the other hand, the usefulness of the PS technique is shown over the same region for external SAR calibration purposes. This process simulates the PS-assisted calibration procedure to be applied systematically to Sentinel-1 SAR products.     

Detecting a moorland wildfire scar in the Peak District,UK, using synthetic aperture radar from ERS-2 and Envisat ASAR

Wildfires occur annually in UK moorland environments, especially in drought years. They can be severely damaging to the ecosystem when they burn deep into the peat, killing ground-nesting birds and releasing CO₂ into the atmosphere. Synthetic aperture radar (SAR) was evaluated for detecting the 18 April 2003 Bleaklow wildfire scar (7.4 km²). SAR’s ability to penetrate cloud is advantageous in this inherently overcast area. SAR can provide fire scar boundary information which is otherwise labour intensive to collect in the field using a global positioning system (GPS). This article evaluates the potential of SAR intensity and InSAR coherence to detect a large peat moorland wildfire scar in the Peak District of northern England. A time-series of pre-fire and post-fire ERS-2 and advanced synthetic aperture radar (ASAR) Single Look Complex (SLC) data were pre-processed using SARScape 4.2 to produce georeferenced greyscale images. SAR intensity and InSAR coherence values were analysed against Coordinate Information on the Environment (CORINE) land‐cover classes and precipitation data. SAR intensity detected burnt peat well after a precipitation event and for previous fire events within the CORINE peat bog class. For the 18 April 2003 fire event, intensity increased to 0.84 dB post-fire inside the fire scar for the peat bog class. InSAR coherence peaked post-fire for moors and heathland and natural grassland classes inside the fire scar, but peat bog exposed from previous fires was less responsive. Overall, SAR was found to be effective for detecting the Bleaklow moorland wildfire scar and monitoring wildfire scar persistence in a degraded peat landscape up to 71 days later. Heavy precipitation amplified the SAR fire scar signal, with precipitation after wildfires being typical in UK moorlands. Further work is required to disentangle the effects of fire size, topography, and less generalized land‐cover classes on SAR intensity and InSAR coherence for detecting fire scars in degraded peat moorlands.     

Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume

The feasibility of interferometric SAR (INSAR) coherence observations for stem volume (biomass) retrieval is investigated by applying coherence data determined from 14 ERS-1 and ERS-2 C-band SAR image pairs. The image set covers a single forested test area in Finland, and both summer (snow-free) and winter conditions are represented. The data set enabled (a) the study of stem volume retrieval performance under varying conditions, (b) the analysis of the seasonal behavior of interferometric coherence, and (c) the determination of the accuracy characteristics of empirical (nonlinear) coherence modeling. Additionally, a new technique to estimate forest stem volume from INSAR data was developed based on constrained iterative inversion of the applied empirical model. The results indicate that the usability of winter images with snow-covered terrain is superior to that of images obtained under summer conditions. The applied empirical model appears to be adequate for describing the stand-wise coherence of boreal forest. Hence, a practical stem volume estimation method can be established based on it. The highest correlation coefficient between the estimated stem volume and the ground truth stem volume showed values as high as r=0.9 and a relative RMSE level of 48% was obtained, respectively.     

基于小波域HMT模型InSAR干涉图噪声滤波研究

相干斑噪声是InSAR干涉图固有的,且InSAR干涉图对相干斑噪声十分敏感,为了得到精度更高的干涉测量产品,需要对干涉图进行相干斑噪声滤波。针对InSAR干涉图中相干斑噪声的统计特性,提出了一种基于小波域HMT模型的InSAR干涉图滤波算法,对InSAR干涉图的实部和虚部分别进行处理。试验研究结果表明,该方法在有效抑制相干斑的同时,还能有效地保持相位的细节信息和条纹的边缘结构,而且大大地减少了残余点的数量,有利于提高InSAR干涉测量的精度。     

Wetland classification using Radarsat-2 SAR quad-polarization and Landsat-8 OLI spectral response data: a case study in the Hudson Bay Lowlands Ecoregion

Circumboreal Canadian bogs and fens distinguished by differences in soils, hydrology, vegetation and morphological features were classified using combinations of Radarsat-2 synthetic aperture radar (SAR) quad-polarization data and Landsat-8 Operational Land Imager (OLI) spectral response patterns. Separate classifications were conducted using a traditional pixel-based maximum likelihood classifer and a machine learning algorithm following an object-based image analysis (OBIA). This study focused on two wetland classes with extensive coverage in the area (bog and fen). In the pixel-based maximum likelihood classification, accuracy increased from approximately 69% user’s accuracy and 79% producer’s accuracy using Radarsat-2 SAR data alone to approximately 80% user’s accuracy and 87% producer’s accuracy using Landsat-8 OLI data alone. Use of the Radarsat-2 SAR and Landsat-8 OLI data following principal components analysis (PCA) data fusion did not result in higher pixel-based maximum likelihood classification accuracy. In the object-based machine learning classification, higher bog and fen class accuracies were obtained when using Radarsat-2 and Landsat OLI data individually compared to the equivalent pixel-based classification. Subsequently, a PCA-data fusion product outperformed the individual bands of the Radarsat-2 and Landsat-8 imagery in object-based classification. Greater than 90% producer’s accuracy was obtained. The margin of error (MOE) was less than 5% in all classifications reported here. Further research will examine alternative data fusion techniques and the addition of Radarsat-2 SAR interferometric digital elevation model (DEM)-based geomorphometrics in object-based classification of different morphological types of bogs and fens.     

C波段SAR山区积雪面积提取研究

合成孔径雷达(SAR)不仅具有穿云透雾,全天候观测地表的能力,而且可穿透地表覆盖一定深度获取地表覆盖物内部特征信息。利用2011年10景ENVISAT\|ASAR可变极化模式精细图像(ASA_APP_1P)数据,分析比较了黑河上游祁连山冰沟流域不同时段积雪SAR后向散射特性,应用同期的MODIS积雪面积产品确定研究区积雪的累积和消融背景信息。研究表明:由于融雪期积雪含水量上升,SAR图像后向散射系数相比干雪或无雪图像明显降低,经过分析认为广泛应用的-3 dB阈值会明显低估湿雪覆盖范围,-2 dB阈值更适合该地区湿雪面积参数提取。山区积雪融化过程中低海拔区域积雪融化而高海拔山区积雪仍可能为干雪,在提取湿雪像元的基础上,根据Sigmoid函数阈值获取的像元湿雪百分比及DEM信息来提取干雪像元,最终获取整个流域积雪面积信息。通过与Landsat ETM+图像积雪面积分类结果进行比较,总体精度达到78%。积雪累积和消融背景信息的分析表明:误差主要源于流域东北部与西北部低海拔区域积雪快速消融。     

PSI technique for analysing slow landslides: a useful tool to handle with care

Persistent scatterers interferometry (PSI) based on the analysis of satellite synthetic aperture radar (SAR) data in the field of landslide mapping is becoming a widespread tool, commonly used together with traditional geomorphological survey techniques and other monitoring instruments. Having acquired permanent scatterers interferometry SAR (PSInSAR?) data since 2005, the Region of Liguria has in recent years carried out several operational tests to define the correct procedures to provide appropriate interpretations of PSI data sets with respect to landslide mapping and state of activity definition. These experiences have resulted in the elaboration of a semi-automatic procedure using spatial analysis tools provided by any commercial geographic information system (GIS) software to allow a quick overview of huge data and obtain indications of potentially unstable areas. An analysis of the results shows a good general congruity between the potentially unstable areas detected and landslide inventory maps, but also some anomalies.