Satellite radar interferometry for monitoring subsidence induced by longwall mining activity using Radarsat-2, Sentinel-1 and ALOS-2 data

This paper describes the simulation and real data analysis results from the recently launched SAR satellites, ALOS-2, Sentinel-1 and Radarsat-2 for the purpose of monitoring subsidence induced by longwall mining activity using satellite synthetic aperture radar interferometry (InSAR). Because of the enhancement of orbit control (pairs with shorter perpendicular baseline) from the new satellite SAR systems, the mine subsidence detection is now mainly constrained by the phase discontinuities due to large deformation and temporal decorrelation noise.This paper investigates the performance of the three satellite missions with different imaging modes for mapping longwall mine subsidence. The results show that the three satellites perform better than their predecessors. The simulation results show that the Sentinel-1A/B constellation is capable of mapping rapid mine subsidence, especially the Sentinel-1A/B constellation with stripmap (SM) mode. Unfortunately, the Sentinel-1A/B SM data are not available in most cases and hence real data analysis cannot be conducted in this study. Despite the Sentinel-1A/B SM data, the simulation and real data analysis suggest that ALOS-2 is best suited for mapping mine subsidence amongst the three missions. Although not investigated in this study, the X-band satellites TerraSAR-X and COSMO-SkyMed with short temporal baseline and high spatial resolution can be comparable with the performance of the Radarsat-2 and Sentinel-1 C-band data over the dry surface with sparse vegetation.The potential of the recently launched satellites (e.g. ALOS-2 and Sentinel-1A/B) for mapping longwall mine subsidence is expected to be better than the results of this study, if the data acquired from the ideal acquisition modes are available.     

极化分解后多纹理特征的建筑物损毁评估

准确地获知灾区的建筑物损毁程度能为抗震救灾和灾后重建提供决策依据。利用震后极化合成孔径雷达(SAR)数据,该文提出了一种综合利用极化分解后多纹理特征的震后建筑物损毁评估方法。首先,用Pauli分解的π/4偶次散射分量剔除非建筑区;其次,用Pauli分解的π/4偶次散射分量的方差特征、对比度特征和Pauli分解的奇次散射分量的对比度特征识别倒塌建筑物,并分别基于区块计算建筑物损毁指数;最后,综合3个纹理特征完成建筑物的损毁评估。采用玉树震后RADARSAT-2数据和东日本大地震后ALOS-1数据的实验验证了所提方法对建筑物损毁评估的有效性,该方法对玉树城区和日本石卷城区的重度、中度和轻度损毁建筑评估的总体精度分别为74.39%和80.26%。与其他方法的对比实验表明,该方法能减少取向角的影响,对存留有少数与方位向平行的完好建筑物的倒塌区、大取向角的完好建筑区的评估更为准确。     

Remote detection of geological structures: an application to the Aravalli region,western India

Optical and microwave remote sensing data are used in conjunction with a digital elevation model to map lineaments in the central parts of the Aravalli region, Rajasthan, western India. Lineament maps interpreted from each data-set are subsequently combined to derive a composite lineament map of the area. Rose plots are used to identify the prominent trends of the lineaments and compared with published structural map of the study area. Three major trends are identified, namely, the NE–SW, NNE–SSW and EW, which are interpreted to be, related to the DF1, DF2 and DF4 deformation phases identified by the previous workers through field studies. The lineaments are classified as fold axes or faults, and a total of 10-fold axes and 30 faults mapped in the area.     

高分辨率SAR影像道路交叉口自动提取方法

针对高分辨率SAR影像道路交叉口提取易受周围地物干扰的问题,设计了一种利用三角形检测模型提取道路交叉口的方法。该方法首先采取滤波与锐化消除道路交叉口区域噪声点并凸显道路边缘;接着根据灰度特征,通过形态学运算确定道路交叉口候选区域;最后在候选道路交叉口区域运用三角形检测模型,确认道路交叉口。使用3 m Tan DEM-X数据进行实验,结果证明算法对各种常见二维道路交叉口模型均有较好识别效果。     

机载X波段极化干涉SAR数据非监督分类方法研究

针对经典极化分类算法在处理机载X波段SAR数据时将过多地物分为体散射类型,并且容易受噪声影响,分类结果存在大量误分现象的问题,通过对机载X波段SAR数据非监督分类方法的研究,提出将极化干涉信息用于机载X波段极化干涉SAR数据的分类。通过运用极化干涉数据进行目标分解得到参数A1和A2对数据进行初始分类,然后结合改进的Wishart最大似然分类算法来进行地物的自适应分类。实验结果表明,该方法能有效避免平地效应的影响,抗噪性好,能正确区分三种典型散射类型,分类效果明显优于极化分类效果。     

MiniSAR与无人机系统集成应用研究

近年来无人机机载合成孔径雷达（SAR）技术发展迅速,成为全天时、全天候数据获取的重要手段.本文对无人机机载合成孔径雷达国外发展应用和国内研究进展进行了阐述,对Mini SAR载荷与无人机飞行平台系统集成进行了测试研究,并验证了在黄河冰情凌汛应急监测的适用性,最后指出了无人机机载Mini SAR在行业应用中的价值.     

NSCT域统计模型的SAR图像相干斑抑制

本文对SAR图像在引进加性模型的基础上,采用非下采样Contourlet变换,根据其系数、邻域系数及父系数3者之间的相关性,给出一个分类准则,把系数分为2类:重要系数和非重要系数,然后采用改进的Donoho阈值处理重要系数,估计出不含噪声的非下采样Contourlet变换系数,从而得到抑制了相干斑的SAR图像。对真实SAR图像进行相干斑噪声抑制实验,结果显示本文方法在抑斑效果和图像的细节保留上均优于目前的许多方法。     

Modeling and sensitivity analysis of navigation parameter errors for airborne synthetic aperture radar stereo geolocation

For the high-resolution airborne synthetic aperture radar (SAR) stereo geolocation application, the final geolocation accuracy is influenced by various error parameter sources. In this paper, an airborne SAR stereo geolocation parameter error model, involving the parameter errors derived from the navigation system on the flight platform, has been put forward. Moreover, a kind of near-direct method for modeling and sensitivity analysis of navigation parameter errors is also given. This method directly uses the ground reference to calculate the covariance matrix relationship between the parameter errors and the eventual geolocation errors for ground target points. In addition, utilizing true flight track parameters’ errors, this paper gave a verification of the method and a corresponding sensitivity analysis for airborne SAR stereo geolocation model and proved its efficiency.     

Classification from Landsat TM of Indurated Volcanic Materials (tepetates) of the Mexican Neo‐volcanique Belt

Abstract

Tepetates, altered indurated and sterile volcanic tuffs, are located among the neo‐volcanic belt in central Mexico. They are divided into three classes according to the erosion level: outcrop tepetates, discontinuous tepetates (in process of erosion), and underlying tepetates (related to fragile areas with high erosion risk). For local communities and peasants, the existence of tepetates is a heavy constraint, with the impossibility to cultivate the land as it is. In order to know the localization and extension of tepetates, we experimented with various classification techniques of a Landsat Thematic Mapper satellite image. Masking techniques were used, assisted both by a DEM and photo interpretation, in addition to radiometric segmentation to obtain a map of tepetates in a study area. The resulting map showed the location of outcrop tepetates well, in agreement with existing soil maps, but underestimated underlying tepetates and high‐risk erosion localities, due mainly to the heterogeneity of the classes at the scale used (30 m pixel).