共查询到20条相似文献，搜索用时 31 毫秒
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星载高分辨率光学图像与SAR图像广泛应用于城市建筑物高度提取,但光学图像存在缺少相关卫星参数的情况,而SAR图像则存在散射特征不完整以及提取效率低等缺陷。针对以上问题,本文提出一种联合高分辨率星载光学与SAR图像的城市大面积建筑物高度快速提取方法。首先,结合支持向量机(SVM)和形态学阴影指数(MSI)快速提取光学图像中的阴影并自动测量阴影长度;之后选择多个合适样本,基于模型匹配法从SAR图像中提取高度;最后将高度与阴影长度作线性回归分析,建立数学模型来提取其他建筑物的高度。该方法将不同卫星系统的数据和特征相结合,互相弥补各自缺陷,不仅提高了效率、降低了成本,同时满足精度要求。 相似文献
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Provides a first assessment of a coregistration technique suitable for multiangle fine spatial resolution synthetic aperture radar (SAR) images. The technique is based on crossroad and road junction extraction and matching and exploits recently introduced road extraction routines for SAR data. These features are matched using relational and geometrical analysis. Results are encouraging and show the possibility to exploit multiangle SAR available from future airborne and satellite missions. 相似文献
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传统光学遥感技术手段在森林覆盖区难以准确获取林下地形,原因在于其只能测量森林冠层顶部高程。微波信号能够穿透森林冠层并记录森林垂直结构信息,为解决林下地形测绘难题带来了契机,如何准确获取林下地形已成为微波遥感领域的研究热点。首先介绍了面向林下地形测绘的合成孔径雷达（synthetic aperture radar,SAR）干涉测高原理及数据获取手段。然后对利用SAR进行林下地形测绘的方法进行了分类,主要包括基于合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）、极化合成孔径雷达干涉测量（polarimetric InSAR,PolInSAR）及基于多基线InSAR/PolInSAR数据的层析SAR（tomographic SAR,TomoSAR）技术的林下地形测绘方法,并介绍了上述3种方法的应用进展。最后在此基础上,从数据获取、误差改正及散射模型构建3个角度分析了林下地形测绘所面临的问题。 相似文献
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针对目前SAR干涉测量中基线估计现存的问题,提出了利用Kalman滤波和配准参数进行基线估计的方法.所提出的方法具有不需地面控制点、不受地形限制和不依赖于轨道参数等优点,并可以估计时变的基线参数.利用南京地区的ERS-1/2 tandem数据进行了试验研究,并对提出的方法进行了验证.结果表明,在精确的卫星轨道数据和地面控制点不能获取时,所提出的方法仍能有效地估计InSAR基线.这在一定程度上补偿了轨道偏移带来的误差,为获取高精度的DEM奠定了基础. 相似文献
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Alireza Sharafzadeh Ali Esmaeily Maryam Dehghani 《Journal of the Indian Society of Remote Sensing》2018,46(11):1785-1793
Synthetic aperture radar (SAR) is a newly-developed remote sensing technology that works in all weather and independent of daylight. Recent satellite designs such as TerraSAR-x, which have resolutions of a couple of meters and sub-meters, have provided appropriate data for modelling and monitoring of urban areas. Image classification and height information extraction is possible considering the nature of SAR data. In this paper, a proper classification method for high-resolution SAR images has been used in urban areas. This classifier is based on statistical models. First, statistical models that are well adapted to urban SAR images are selected. Initial labelling is performed using the maximum likelihood method. A method based on Markov random fields is applied to improve the results by considering neighbourhood information. Meanwhile, topographic information is extracted using the phase difference obtained from SAR interferometry. After classification and height extraction, the homogeneous regions consisting of locations with similar objects are determined. The homogeneous region adjacency graph are generated using vectors containing classification information, extracted objects, height of pixels forming each region, and information on the neighbouring areas. Height and classification information are then merged by assigning height conditions based on the nature of objects and optimizing an energy function. The results obtained, including buildings, streets, and corner reflectors, are easily recognizable. The overall accuracy is improved from 57% in the initial classification to 95% in the employed procedure. Moreover, the accuracy of height estimation is about 2.74 m, which is acceptable for height estimations of buildings with more than one floor. 相似文献
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将卫星雷达遥感应用于滑坡灾害的探测与监测,不仅可以从空间尺度上大范围捕捉到滑坡信号,而且可以从时间尺度上以较长周期追踪滑坡的运动状态。但是,卫星雷达遥感本身的局限性和滑坡所处的复杂地形环境使这一应用面临一些挑战。对卫星雷达遥感技术的4个主要挑战进行了总结与分析,同时给出了相应的解决方案:①通过提高卫星雷达影像的空间、时间分辨率,使用较长波段雷达信号或采用增强型时间序列分析技术,可降低密集植被覆盖对相干性的影响。另外,采用像素点偏移量追踪或距离向分频干涉测量方法,可克服传统干涉测量中大梯度形变引起的相位失相干。②大气延迟对卫星遥感的影响较大,尤其是地处山区的滑坡探测和监测,利用通用型卫星雷达大气改正系统可显著减弱干涉影像的大气信号并进一步简化时间序列分析,提高缓慢运动滑坡的探测和监测质量。③对于中等分辨率的雷达影像而言,利用数字高程模型可提前量化分析雷达几何畸变（如叠掩、阴影等）引发的滑坡探测监测的适用性;而对于高分辨率的雷达影像而言,利用机器学习方法无需外部高分辨率数字高程模型即可精确识别雷达影像的阴影和叠掩区并进行掩膜,从而大幅度提高数据处理效率。④针对高坡度地区残余的地形相位引起的解缠误差,可通过基线线性组合的方法予以减弱。此外,提出了一个基于多源对地观测的滑坡探测/监测系统框架,综合卫星雷达遥感与其他对地观测数据（如地基雷达、激光雷达、全球导航定位系统）,搭建了一个自动化滑坡探测与监测系统。该研究旨在阐明卫星雷达遥感的优缺点,进一步深化其在滑坡灾害监测方面的应用和推广,引出未来侧重发展方向的思考与探讨。 相似文献
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Synthetic Aperture Radar (SAR) data are of high interest for different applications in remote sensing specially land cover classification. SAR imaging is independent of solar illumination and weather conditions. It can even penetrate some of the Earth’s surface materials to return information about subsurface features. However, the response of radar is more a function of geometry and structure than a surface reflection occurs in optical images. In addition, the backscatter of objects in the microwave range depends on the frequency of the band used, and the grey values in SAR images are different from the usual assumption of the spectral reflectance of the Earth’s surface. Consequently, SAR imaging is often used as a complementary technique to traditional optical remote sensing. This study presents different ensemble systems for multisensor fusion of SAR, multispectral and LiDAR data. First, in decision ensemble system, after extraction and selection of proper features from each data, crisp SVM (Support Vector Machine) and Fuzzy KNN (K Nearest Neighbor) are utilized on each feature space. Finally Bayesian Theory is applied to fuse SVMs when Decision Template (DT) and Dempster Shafer (DS) are applied as fuzzy decision fusion methods on KNNs. Second, in feature ensemble system, features from all data are applied on a cube. Then classifications were performed by SVM and FKNN as crisp and fuzzy decision making system respectively. A co-registered TerrraSAR-X, WorldView-2 and LiDAR data set form San Francisco of USA was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with different sensor improves classification results for most of the classes. 相似文献
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《International Journal of Digital Earth》2013,6(8):671-687
Remote-sensing data play an important role in extracting information with the help of various sensors having different spectral, spatial and temporal resolutions. Therefore, data fusion, which merges images of different spatial and spectral resolutions, plays an important role in information extraction. This research investigates quality-assessment methods of multisensor (synthetic aperture radar [SAR] and optical) data fusion. In the analysis, three SAR data-sets from different sensors (RADARSAT-1, ALOS-PALSAR and ENVISAT-ASAR) and optical data from SPOT-2 were used. Although the PALSAR and the RADARSAT-1 images have the same resolutions and polarisations, images are gathered in different frequencies (L and C bands, respectively). The ASAR sensor also has C-band radar, but with lower (25 m) resolution. Since the frequency is a key factor for penetration depth, it is thought that the use of different SAR data might give interesting results as an output. This study describes a comparative study of multisensor fusion methods, namely the intensity-hue-saturation, Ehlers, and Brovey techniques, by using different statistical analysis techniques, namely the bias of mean, correlation coefficient, standard deviation difference and universal image quality index methods. The results reveal that Ehlers' method is superior to the others in terms of spectral and statistical fidelity. 相似文献
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基于RPC的TerraSAR-X影像立体定向平差模型 总被引：1，自引：0，他引：1
针对新型高分辨率雷达卫星TerraSAR-X立体像对,本文提出采用基于RPC的平差模型,通过少量的地面控制点来拟合因传感器不稳定、平台星历数据不精确及测距误差引起的影像几何畸变,从而达到精确定向目的.为验证RPC平差模型的适用性,通过在立体成像区域均匀市设人工角反射器点的方法验证其模型精度,并评估了其三维定向平差后的精... 相似文献
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高分三号卫星全极化SAR影像九寨沟地震滑坡普查 总被引：1，自引：1，他引：0
基于光学遥感影像的区域滑坡普查易受云雾天气的影响,存在滑坡体调查不全面的问题,无法满足震后应急调查与恢复重建的需求。本文提出了一种极化SAR卫星数据滑坡普查方法,采用高分三号全极化SAR卫星影像数据,以九寨沟地震震区为实验区,在深入分析滑坡体和其他地物类型散射特征的基础上,融合极化特征、纹理特征和地形特征等多维特征信息,结合高分二号影像获取的训练样本,构建基于BP神经网络的全极化SAR数据滑坡自动识别模型,实现滑坡体的自动快速识别。与高分辨率光学影像与无人机航空影像目视解译结果相比较,总体识别精度为92.8%,Kappa系数为0.715,识别准确度满足地震应急实际应用的需求。研究成果可用于震区大区域滑坡体的普查,为后续开展无人机高分辨率影像滑坡体详查、灾后应急与景区恢复提供辅助信息支撑,并促进国产高分SAR卫星数据在防震减灾中的应用。 相似文献
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Peter Reinartz Rupert MüllerPeter Schwind Sahil SuriRichard Bamler 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(1):124-132
Orthorectification of satellite data is one of the most important pre-processing steps for application oriented evaluations and for image data input into Geographic Information Systems. Although high- and very high-resolution optical data can be rectified without ground control points (GCPs) using an underlying digital elevation model (DEM) to positional root mean square errors (RMSEs) between 3 m and several hundred meters (depending on the satellite), there is still need for ground control with higher precision to reach lower RMSE values for the orthoimages. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This is due to the fact that the SAR antenna measures distances which are mainly dependent on the terrain height and the position of the satellite. The latter can be measured with high precision, whereas the satellite attitude need not be known exactly. If the used DEM is of high accuracy, the resulting geocoded SAR data are very precise in their geolocation. This precision can be exploited to improve the orientation knowledge and thereby the geometric accuracy of the rectified optical satellite data. The challenge is to match two kinds of image data, which exhibit very different geometric and radiometric properties. Simple correlation techniques do not work and the goal is to develop a robust method which works even for urban areas, including radar shadows, layover and foreshortening effects. First the optical data have to be rectified with the available interior and exterior orientation data or using rational polynomial coefficients (RPCs). From this approximation, the technique used is the measurement of small identical areas in the optical and radar images by automatic image matching, using a newly developed adapted mutual information procedure followed by an estimation of correction terms for the exterior orientation or the RPC coefficients. The matching areas are selected randomly from a regular grid covering the whole imagery. By adjustment calculations, parameters from falsely matched areas can be eliminated and optimal improvement parameters are found. The original optical data are orthorectified again using the delivered metadata together with these corrections and the available DEM. As proof of method the orthorectified data from IKONOS and ALOS-PRISM sensors are compared with conventional ground control information from high-precision orthoimage maps of the German Cartographic Survey. The results show that this method is robust, even for urban areas. Although the resulting RMSE values are in the order of 2-6 m, the advantage is that this result can be reached even for optical sensors which do not exhibit low RMSE values without using manual GCP measurements. 相似文献
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洪涝灾害给社会、经济造成巨大损失,及时、快速监测洪涝范围在抗灾救灾中具有重要意义。合成孔径雷达(SAR)由于其主动式微波成像的机理,可为全天时、全天候、大范围洪涝灾害监测提供支持。本文首先以高分三号(GF-3)卫星影像为数据源,基于灰度共生矩阵(GLCM)、局部二值模式(LBP)等6种纹理描述方法提取138个SAR影像纹理特征;然后利用随机森林(RF)指标重要性评估功能,筛选出重要性得分较高的纹理特征进行水体信息提取;最后结合数学形态学对初始水体提取结果进行后处理,评估安徽巢湖附近区域洪涝灾害。试验表明,本文方法的水体提取精度优于传统阈值法(Otsu)及分类算法(KNN和SVM),可有效提取洪涝灾害的影响范围,为选取合适的SAR影像纹理特征进行洪涝范围快速监测提供参考。 相似文献
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相对于星载和机载SAR而言,使用车载SAR数据提取DEM可有效缩短DEM数据更新周期,降低成本。由于车载SAR平台结构可变,干涉定标需要重复进行,本文提出了基于单控制点的车载双天线干涉SAR的DEM提取方法。该方法基于单个控制点的精确坐标信息进行相位、斜距和高程改正,并实现高程的迭代求解,无需布设大量控制点进行干涉定标,有效简化外业工作。仿真试验证明：当基线长度估计达到毫米级精度,基线倾角估计也达到较高的精度时,可保证该方法较高的DEM提取精度。利用2018年在湖北武汉地区获取的车载双天线干涉SAR数据使用该方法和基于干涉定标的多控制点方法进行DEM提取对比试验,结果表明该方法在检查点的高程中误差为0.301 8 m,多控制点方法为0.258 4 m,在高相干区域,两种方法的DEM结果具有高度的统一性。 相似文献
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Guoxiang Liu Jonathan Li Zhu Xu Jicang Wu Qiang Chen Huixin Zhang Rui Zhang Hongguo Jia Xiaojun Luo 《International Journal of Applied Earth Observation and Geoinformation》2010
The Ms8.0 Wenchuan earthquake (in China) occurred on 12 May 2008 as a result of slip on the northeastern-striking Longmen Shan (LMS) faults beneath the rugged margin between the Qinghai-Tibet Plateau and Sichuan Basin. The catastrophic event caused significant surface ruptures and permanent ground displacement in a wide area. This paper concentrates on mapping surface deformation caused by the main shock with the interferometric synthetic aperture radar (InSAR) technology. The coseismic interferogram covering an area of over 83,000 km2 is computed with use of 46 SAR images that were collected along 6 adjacent ascending orbits by the L-band SAR sensor onboard the Japanese Advanced Land Observing Satellite (ALOS). The displacements measured at 16 GPS sites are used to check the accuracy of the InSAR deformation measurements. The radar coherence is computed and analyzed in relation to the topography and the normalized difference vegetation index (NDVI) estimated from the Landsat-7 imagery. The results show that the coseismic surface deformation can be mapped up to a centimeter-accuracy level even over the highly mountainous and heavily vegetated area with the L-band interferometer. It is also demonstrated that the L-band interferograms with time interval of months to years can still maintain acceptable radar coherence for deformation extraction over the area under the extreme conditions. The extracted InSAR deformation measurements show that the lands in the Sichuan Basin had moved 0.1–1.3 m toward the satellite along the radar line of sight (LOS) direction with an azimuth of 349.8° and an elevation angle of 51.3°, while the lands in the LMS area had moved 1.4 m at most away from the satellite. 相似文献
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本文介绍了InSAR卫星轨道状态矢量内插方法,基于荷兰Delft大学开发的Doris雷达干涉软件分析了SAR卫星轨道数据误差对基线参数、参考椭球面相位、地形干涉相位和数字高程模型(DEM)精度的影响。以西藏玛尼地区为例,采用ERS1/2卫星数据,利用Doris软件,分别生成了基于欧空局(ESA)粗略轨道数据和荷兰Delft大学精密轨道数据的数字高程模型(DEM),并以SRTMDEM为基准对其精度进行了对比分析。结果表明,基于粗轨数据获取的DEM明显存在系统偏差,而基于精轨数据获取的DEM与SRTM DEM吻合的很好,相对于前者,精度提高5倍。 相似文献
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提出一种改进的永久散射体PS(Persistent Scatterer)探测方法,首先基于时序SAR影像的振幅信息,利用振幅和振幅离差指数双阈值法获取PS候选点,然后根据干涉相位空间相关性,对所有候选点进行相位稳定性分析,筛选出既满足雷达反射强度高、又符合散射稳定性要求的真实PS点,最后基于所选PS点提取形变信息。选取上海市局部区域作为实验区,以16幅TerraSAR-X(TSX)影像为数据源,进行PS探测和形变信息提取。结果表明,相比于已有的PS探测方法,本文方法能够更准确、可靠地识别出时序SAR影像中的PS点。形变结果显示,研究区域内最大沉降速率为-51mm/a,平均沉降速率为-15.8mm/a,以水准实测数据进行检核,验证了基于改进的PS探测方法进行地表形变信息提取结果的可靠性。 相似文献
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遥感影像融合作为影像处理领域中最具有挑战的工作,一直是学术界研究的热点。合成孔径雷达SAR(Synthetic Aperture Radar)具备全天时、全天候、穿透云雾等多种特点,却因存在相干斑噪声等问题,使得影像难以解译。相比之下,光学影像可以反映地物的光谱和空间信息,易于解译,但容易受到云雾干扰,造成信息丢失,将光学与SAR影像数据融合可以实现不同类型传感器成像之间的信息互补,能够更好地为后续的影像分析与解译提供方便。本文首先对光学和SAR影像融合进行了系统性回顾,包括传统融合方法和基于深度学习方法在影像融合方面的最新工作,重点阐述了卷积神经网络CNN (Convolutional Neural Network)、生成式对抗网络GAN (Generative Adversarial Networks)等框架在光学和SAR影像融合中的进展;然后总结了光学和SAR影像融合在深度学习领域开发的数据集,并做了简单介绍和说明;最后,从数据集、时间序列影像融合、融合评价体系和算法轻量化等4个方面对光学和SAR影像融合的未来发展趋势进行了展望。 相似文献