共查询到16条相似文献，搜索用时 171 毫秒
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由于SAR斜距成像几何方式及地形起伏的影响,原始SAR影像存在透视收缩、叠掩、阴影等严重的几何畸变和辐射畸变。其中,叠掩区具有强烈的后向散射回波,在极化SAR影像的分类研究中容易造成林地与居民地等地物混分,降低分类的精度。针对该问题,本文研究一种地形辐射校正方法,引入投影角计算后向散射系数γ0,有效地解决了地形起伏造成的辐射畸变问题。选取一景全极化Radarsat-2影像进行实验验证,分别对地形辐射校正前后的极化SAR影像进行了复Wishart监督分类。通过对分类结果的比较,表明经本文地形辐射校正方法处理后,极化SAR影像的分类精度得到了改善。 相似文献
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地形引起的雷达辐射畸变及其校正 总被引：3，自引：3，他引：3
合成孔径雷达影像由于其侧视特点 ,存在着严重的地形引起的几何畸变及辐射畸变。辐射畸变不仅对 SAR辐射标定造成困难 ,而且严重影响了影像分类、土壤湿度信息提取、森林蓄积量信息提取等应用。本文将辐射畸变归结为面积效应和局部入射角效应 ,推导了散射面积归一化因子 ,以消除辐射畸变的面积效应。提出了一种以局部入射角的线性函数表达的后向散射模型 ,在此基础上 ,给出了消除局部入射角效应的校正函数。最后 ,以RADARSAT SAR影像进行地形辐射畸变校正的试验与分析 相似文献
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在山区获取地面控制点比较困难,运用模拟SAR进行配准、校正,具有较大的优势。本文在分析SAR成像几何结构及ALOS PALSAR卫星轨道参数特征的基础上,运用RD定位模型对DEM的每个网格点进行雷达成像点的位置计算,模拟SAR图像,并提取当地入射角、投影角及规则化因子等;模拟出的SAR图像与真实SAR图像纹理吻合,有利于控制点的自动配准。在此基础上对ALOS PALSAR进行编码,构建基于规则化因子及入射角的地形辐射校正模型,消除面积效应及地形起伏造成的畸变问题,从结果中分析,校正后的图像明暗差异明显减少,这对雷达定量反演研究具有一定的现实意义。 相似文献
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机载干涉SAR测绘制图应用系统研究 总被引：7，自引：1，他引：6
介绍我国机载干涉SAR系统及测绘应用概况,介绍机载干涉SAR生成DEM、SAR正射校正、地形引起的SAR后向散射畸变校正、机载SAR图像解译、雷达图像与光学影像的融合、雷达"空中三角测量"等关键技术研究进展.最后,介绍机载干涉SAR系统在泰山试验区的测绘制图实验情况. 相似文献
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随着TerraSAR-X、COSMO-SkyMed等新型高分辨率SAR卫星的成功发射,雷达遥感进入快速发展阶段,对高分辨率SAR影像模拟存在大量需求。利用一种基于简化定位模型的方法对TerraSAR-X影像进行模拟,只利用DEM数据就可以模拟出任意平台高度、任意入射角、任意分辨率、任意视向和飞行方位角情况下的SAR影像和叠掩、阴影情况,而且不需要精确的卫星轨道参数和系统参数,通过与真实影像的对比,证明方法的有效性。文中方法可以在高分辨率SAR数据选择、测试不同成像参数对雷达影像的影响等多个方面应用。 相似文献
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雷达后向散射模型及其在雷达图像地形影响纠正中的应用 总被引：6，自引：0，他引：6
在从雷达估测森林生物量时，经常遇到的一个问题是地形对雷达信号的影响。地形使得雷达波的入射角度改变，使每个雷达图像像元所包含的地表面积改变，由于地面的起伏，植被本身的结构也不同，纠正这种由地形而不是植被类型引起的雷达图像的变化是一个很复杂的问题，除了需要高质量的地形数据外，还必须理解植被雷达信号随地形变化的规律。提出一种可用来模拟森林及其它植被处于山坡上的雷达后向散射模型。结合DEM数据，模拟的结果可用来进行雷达图像的地形影响纠正，如果多极化或多波段图像存在，通过雷达模型可用从一种极化推导出的地形信息来纠正其它极化的图像数据。 相似文献
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星载SAR水下地形和水深遥感的最佳雷达系统参数模拟 总被引：12，自引：1，他引：11
根据星载合成孔径雷达 (SAR)浅海水下地形和水深成像机理 ,建立了浅海水下地形和水深雷达后向散射截面仿真模型。该模型包括奈维 斯托克斯方程、谱作用量平衡方程和雷达后向散射模式。利用该模型仿真结果 ,探讨了不同波段 (P、L、C和X)、不同极化 (VV和HH)和不同入射角 (2 0°— 70°)的星载SAR测量浅海水下地形和水深的能力。研究结果表明 ,浅海水下地形和水深遥感的最佳波段为P波段 ,L波段次之 ,C波段比X波段要好一些。VV极化SAR的测量能力要强于HH极化。 2 0°— 40°是星载SAR测量浅海水下地形和水深的最佳入射角范围。 相似文献
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地基雷达的微波面散射模型对比与土壤水分反演 总被引：1，自引：1，他引：0
为了探究地基合成孔径雷达(c GBSAR)后向散射信号的时空变化规律和研究雷达土壤水分反演的影响因素,在内蒙古闪电河流域的昕元牧场站进行了地基雷达观测试验,本文结合以上观测试验的地基雷达数据进行波段、入射角度、极化通道3个雷达参数以及地表粗糙度参数对雷达的后向散射系数影响的分析,然后利用以上分析结果选择地表微波面散射模型,最后利用选定的地表微波面散射模型构建人工神经网络数据集来反演地表土壤水分。结果表明:(1)在地基雷达视场内,各地表微波面散射模型的模拟结果与地基雷达实测的L波段全极化数据拟合效果最佳的是AIEM-Oh模型。(2)通过对20°—60°范围内的雷达入射角度的AIEM-Oh模型后向散射系数模拟的绝对残差分析发现,雷达入射角为25°、41°和53°时模拟结果最接近雷达实测值。(3)最后通过分析土壤水分反演结果发现,当雷达入射角度为41°时的土壤水分反演精度最高,相关系数R是0.8080,RMSE是0.0385 m~3m~3。本文的结论是雷达后向散射信号受到雷达入射角度和地表粗糙度相互作用的影响,因此通过考虑地表粗糙度来合理的选取雷达入射角能够提高土壤水分的反演精度。 相似文献
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基于图像特征的星载SAR图像模拟研究 总被引：3，自引：0，他引：3
SAR图像模拟技术被广泛应用于SAR系统的设计和验证、SAR图像的正射纠正、雷达图像解译和目标识别等。随着星载SAR的发展,必然面临着对星载SAR图像模拟的大量需求。本文首先从SAR图像的几何特征和辐射特征出发,探讨了SAR图像模拟技术的原理,分析了RD（Rang Doppler）模型,后向散射模型和斑噪模型。在传统RD模型的基础上,根据不同地形特征（起伏地形和平坦地形）考虑不同的后向散射模型。特别强调了在平坦地形情况下,需要地物分类数据的参与,并利用Ulaby和Dobson的后向散射模型。另外,在SAR图像统计特征的基础上,进行SAR图像的乘性噪声模拟,可以满足更逼真的SAR场景需求。然后,给出了图像模拟的算法流程,并对关键步骤的算法做了分析。最后,在实现基于图像特征的星载SAR图像场景模拟算法的基础上,选择新疆窝依牙地区和天津地区分别进行起伏地形和平坦地形的模拟试验,实验结果证明了本文模拟算法的有效性。 相似文献
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Mehdi Hosseini Mohammad Reza Saradjian 《Journal of the Indian Society of Remote Sensing》2014,42(4):719-726
High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively. 相似文献
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研究了一种基于雷达后向散射特性,从单张星载高分辨率SAR影像提取建筑物高度的方法。该方法通过检测建筑物二次散射强度,同时获取目标方位角、入射角等参数以及相关介质特性等信息,根据后向散射几何光学模型估算建筑物高度。实验验证了对于相当一部分典型建筑物都能够获得较为理想的高程估计值,进而证明了该方法提取建筑物高度的可行性和有效性。 相似文献
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Hyangsun Han Jae-In Kim Chang-Uk Hyun Seung Hee Kim Jeong-Won Park Young-Joo Kwon 《地理信息系统科学与遥感》2020,57(5):650-669
ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer. 相似文献
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Estimation of the polarization orientation angle shifts induced by terrain azimuth slope variations is a recently developed application in radar polarimetry. In general, without any prior knowledge on the terrain, two polarimetric SAR (POLSAR) flight passes are required to derive terrain slopes in perpendicular directions for digital elevation model (DEM) generation. Moreover, we note that SAR intensity is a strong indicator of the range component of the terrain slopes. In this letter, we developed a method for DEM generation requiring only one POLSAR flight pass, by combining orientation angle estimation and a shape-from-shading technique. In particular, when limited POLSAR data are available, this POLSAR technique provides an alternative way for DEM generation. National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA/JPL) AIRSAR L-band POLSAR data over Camp Roberts, California, is used to demonstrate the results of the method proposed in this letter, and a DEM derived from simultaneously measured C-band interferometric SAR from NASA/JPL topographic SAR instrument is selected as the comparative ground truth to validate the effectiveness of this single POLSAR method. Analyses and discussions are also included in this letter. 相似文献