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《遥感学报》2000,4(Z1):38-44
MODIS的设计目的是能够每隔1-2天测量一次全球范围生物的和物理的过程.我们可以从该仪器提供的数据中获得进一步的地球表面和低层大气的全球动力和过程信息.对MODIS数据,利用遗传算法,首先从大气温度探测波段中,反演得到大气温度廓线信息;然后从水汽波段中获得水汽廓线信息;最后在已有的温、湿廓线基础上,校正测量辐射亮度数据.校正了大气影响后,利用MODIS29、31、32大气窗口波段,采用比辐射率约束方法(Emissivityboundsmethod)缩小波段比辐射率取值范围,然后采用遗传算法实现陆面温度、波段比辐射率等地表参数的同时反演.模拟试验表明,利用遗传算法可以从MODIS数据中获得较为精确的大气廓线信息和地面参数信息,为全球温室效应和全球变化研究提供支持. 相似文献
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FY-3C/MWHTS资料反演陆地晴空大气温湿廓线 总被引：2，自引：0，他引：2
针对风云三号C星微波湿温探测仪(FY-3C/MWHTS)的陆地晴空观测资料,建立了一维变分反演系统,对大气的温湿廓线进行反演。为了更好地描述温湿廓线的相关性,同时减小温度和湿度在反演过程中相互之间的误差传递,提出了使用背景协方差矩阵的联合矩阵和单独矩阵进行组合反演的方法。对于MWHTS模拟亮温和观测亮温之间的偏差,使用逐扫描点的统计回归方法进行校正。选择中国部分陆地区域的晴空观测亮温进行温湿廓线的反演,并利用欧洲中期天气预报中心(ECMWF)再分析数据、美国国家环境预报中心(NCEP)分析数据以及无线电探空观测(RAOB)数据对反演结果进行验证,温湿廓线的反演结果与ECMWF再分析数据验证的最大均方根误差分别是2.59 K和11.87%,与NCEP分析数据验证的最大均方根误差分别是1.88 K和21.50%,与RAOB数据验证的最大均方根误差分别是3.43 K和25.48%,验证结果表明了反演结果的可靠性。另外与国外同类载荷AMSU观测亮温的物理方法和统计方法反演精度进行了对比,结果表明:MWHTS具有较强的湿度廓线以及高空温度廓线的探测能力,且针对MWHTS的观测亮温建立的一维变分反演系统具有较高的反演精度。与NCEP 6小时预报廓线的验证结果表明:反演的湿度廓线可以提高预报廓线的精度。 相似文献
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2013年中国发射了首颗进行全球导航卫星系统(global navigation satellite system,GNSS)掩星观测的气象卫星风云3号C星(Fengyun-3C,FY-3C),且已发布自2014年6月以来的FY-3C掩星大气产品,但目前还未见将其应用于大气边界层的相关研究。首次尝试利用FY-3C折射率产品确定边界层高度并进一步进行空间分布分析。结果表明,在小波协方差变换法基础上,进行尖锐度约束,能够确定FY-3C掩星低层大气折射率廓线中可能存在的突变,反演边界层高度。所得到的2015―2018年各年边界层高度全球分布在不同纬度及海洋和陆地上的差异基本体现了边界层与地表气候及地形的关系,但FY-3C折射率产品在低层大气的精度和垂直分辨率相对较低。因此,反演成功率总体上较低,反演结果对边界层高度空间分布细节特征的呈现仍有待提升。 相似文献
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利用AIRS观测资料进行红外高光谱大气探测能力试验的研究 总被引：2，自引：0，他引：2
利用EOS Aqua卫星平台上搭载的光栅式高光谱分辨率大气红外探测仪器AIRS（Atmospheric Infrared Sounder）的实际观测资料，采用经验正交函数展开法（特征向量法）进行了大气温度、湿度廓线的反演误差敏感性试验研究。试验内容包括光谱覆盖、通道覆盖、通道宽度等。通过这些试验，研究在中国现有的经济、技术条件下要达到在对流层1km气层温度、湿度分别小于1K和20％的反演精度和垂直分辨率所应该具备的光谱通道特征，为发展中国自己的红外高光谱分辨率探测技术提供科学依据。试验计算结果表明，基于中国现有的经济和技术基础，发展具有100个左右的光谱通道，通道宽度为2-4个波数的干涉式红外大气探测仪器（比中国目前FY-3初期的滤光片式红外分光计的光谱波段宽度减小3-4倍），可明显改善大气参数反演精度，基本满足现阶段使用要求。 相似文献
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《武汉大学学报(信息科学版)》2021,(11)
全球导航卫星系统(global navigation satellite system, GNSS)水汽层析技术凭借高精度、低成本、全天候等优点成为获取高时空分辨率水汽三维分布的重要手段之一。引入遥感卫星提供的高分辨率水汽信息,首次提出附加高水平分辨率大气可降水量(precipitable water vapor, PWV)约束的GNSS水汽层析算法,对现有水汽层析算法的约束条件进行补充和改进。首先对高分辨率PWV观测值进行校正,然后基于二次加密划分的层析体素块构造PWV约束方程,通过将PWV约束方程融合到GNSS层析模型来改善模型的约束条件,进而优化层析结果质量。利用徐州地区2017-08的GNSS观测数据和风云三号A星(Fengyun-3A, FY-3A)遥感水汽数据对该算法的可行性及精度进行验证,分别以高精度的探空水汽廓线和ERA5三维水汽密度场为参考值对层析结果进行评估。实验结果表明,所提算法反演的水汽廓线和三维水汽分布均优于传统层析算法,各类精度指标都有了显著改善,其中平均均方根误差由2.73 g/m~3减小为1.78 g/m~3,反演精度提高了34.80%,进一步表明所提算法可有效改善层析结果质量,有助于获取高精度和高可靠性的三维大气水汽分布。 相似文献
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GPS掩星数据反演中的地球扁率影响改正 总被引：2，自引：1，他引：2
采用CHAMP实测轨道数据和MSISE-90经验干大气模型，分别将地球作为圆球和椭球，前向模拟了附加相位延迟，然后对模拟数据进行反演。通过不同方案下反演温度廓线与大气模型温度廓线的比较，发现实测掩星数据的反演中，忽略地球扁率影响会给温度反演结果带来较大的误差；而采用相应方法对地球扁率影响进行改正后，温度廓线的误差明显减弱，从而证明了改正方法的正确性。 相似文献
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星载红外高光谱传感器温度廓线反演综述 总被引：1，自引：0，他引：1
全球变暖是当前国际社会的热点话题,温度廓线是大气热力状态的重要参数,准确高效地获取其时空分布信息是天气预报和气候变化研究的前提。与传统的地面观测方式相比,卫星探测数据具有高空间、时间分辨率的优势,为气象观测提供了数据支持。本文分别针对星载红外高光谱传感器3种常见观测模式(天底、掩星及临边观测模式)下的国外7个传感器代表(IMG、AIRS、IASI、HALOE、TES、MIPAS、ACE-FTS)及国内大气探测卫星(风云系列和高分系列)在参数、性能和用途等3个方面进行介绍,得出当前国际上天底观测可提供垂直方向1 km内温度产品精度小于1 K,TES临边观测模式可提供的对流层温度廓线产品精度最高可达0.5 K,均满足NWP精度要求,可有效的用于天气预报及气候变化研究;由辐射传输方程出发,对气温廓线反演的3种常用方法 (统计回归、物理及人工神经网络反演)的基本原理、特点及发展历程进行了对比分析。同时,就温度廓线反演算法的关键问题及可能的解决方案进行了阐述;并对误差(平滑、模型参数及测量误差)产生及其传播原理进行系统地归纳总结。最后,提出现阶段气温探测过程中存在的问题。 相似文献
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以高精度和高垂直分辨率探测大气区间对流层顶—平流层底UTLS（Upper Troposphere/Lower Stratosphere,5—35 km）的温度和水汽廓线是提高大气条件变化监测水平的关键,而利用激光掩星方法同时探测温度和水汽则是对现有探测技术的重要补充。本文研究了激光掩星协同探测和反演温度和水汽分子数密度的方法,其中重点研究了双吸收波长近红外激光反演温度的方法,具体为从近红外波段氧气吸收光谱中,选择2个不同跃迁能级对应的特征吸收峰,在每个吸收峰附近各选出1个吸收线,利用2个吸收线对应的双吸收波长激光以及参考线对应波长激光进行掩星探测,进而由探测数据反演出温度。整个温度和水汽协同反演步骤是先反演温度廓线,然后由温度廓线以及5 km参考高度处的压强先验值计算得到压强廓线,最后在温度和压强廓线基础上,结合水汽单吸收波长和参考波长激光掩星数据,反演得到水汽分子数密度廓线。此外,本文对探测和反演过程进行了模拟仿真,通过在近红外波段选择合适的氧气吸收波长和水汽吸收波长,模拟得到掩星透过率数据,以此反演得到温度和水汽分子数密度廓线。并通过对整个过程的分析,明确了反演过程中的误差项及其传递关系,结合数值仿真结果,说明了各个误差项的影响大小。结果显示,在UTLS区间内,温度反演误差总体小于1.05 K,水汽分子数密度反演误差总体小于4%,该误差范围说明了温度和水汽协同反演方法的可行性。 相似文献
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地面三维激光扫描点云角度分辨率研究 总被引：1，自引：0，他引：1
给出了点云角度分辨率近似等于扫描间隔和光斑直径大小的两种取值条件,以及该条件与测角精度的关系、理论最小角度分辨率与测角精度的关系,在此基础上推导出了它们的简化关系式,并利用推导的公式计算分析了29款扫描仪角度分辨率二种取值的条件及其理论最小角度分辨率大小,验证了公式的实用性。 相似文献
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研究了三维激光扫描测量系统对标靶的重复扫描测量精度,得出其内符合精度优于1 mm,精度并没有随着扫描距离的增加而降低。采用一种基于激光空间分布拓扑关系的方法来分别评价高程精度和平面位置精度,从数据统计来看,平面位置内符合精度较高。 相似文献
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There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information. 相似文献
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高分辨率数据中云高度的差异性突显,特别是边缘处高度在云阴影识别和地表辐射估算等方面成为需要考虑的重要因素。热红外数据获取云高度分辨率较低、缺乏细部差异性特征,为解决这一问题,首先将对应的热红外和可见光数据进行特征点配准,再将基于热红外数据计算的云高度重采样至高分辨率,然后以基于欧式距离变换的围线搜索方法及距离加权将热红外云边缘高度匹配至对应的可见光图像,最后根据云阴影的相似度匹配方法确定真实云高度。结果表明,算法在遵循热红外云高信息分布变化规律的同时,可以得到较准确的高分辨率云边缘高度,一定程度上解决了热红外技术获取云高在分辨率上的局限,扩展了其在云高反演方面的作用。 相似文献
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Ruben Valbuena Ana Hernando Jose Antonio Manzanera Eugenio Martínez-Falero Antonio García-Abril Blas Mola-Yudego 《International Journal of Digital Earth》2018,11(12):1205-1218
In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral (MS) sensors, we suggest the inclusion of normalized difference vegetation index (NDVI) metrics along with the more traditional LIDAR height metrics. Here the data fusion method consists of back-projecting LIDAR returns onto original MS images, avoiding co-registration errors. The prediction method is based on non-parametric imputation (the most similar neighbor). Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods. Results show improvements when using combinations of LIDAR and MS compared to using either of them alone. The MS sensor has little explanatory capacity for forest variables dependent on tree height, already well determined from LIDAR alone. However, there is potential for variables dependent on tree diameters and their density. The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity, which are best described from synergies between LIDAR heights and NDVI dispersion. Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes. Their inclusion in the predictor dataset may, however, in a few cases be detrimental to accuracy, and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis. 相似文献
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地面三维激光扫描是城市建筑立面数据采集的新方法,由于三维点云具有数据量大、无规则等特点,导致从点云中精确分割城市建筑物信息面临着严峻的挑战。在传统张量投票方法基础上,充分考虑各个尺度下点云立面特征判别的概率,提出了一种多尺度张量投票方法来实现平面分割,更加精确地实现了立面特征的识别。通过实例,将该方法同主成分分析和传统张量投票进行对比与分析,结果表明,多尺度张量投票方法在分割精度方面较优。 相似文献
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Traditional field-based forest inventories tend to be expensive, time-consuming, and cover only a limited area of a forested region. Remote sensing (RS), especially airborne laser scanning (ALS) has opened new possibilities for operational forest inventories, particularly at the single-tree level, and in the prediction of single-tree characteristics. Throughout the world, forests have varying characteristics that necessitate the development of modern, effective, and versatile tools for ALS data processing. To address this need, we aimed to develop a tool for individual tree detection (ITD) utilising a self-calibrating algorithm procedure and to verify its accuracy using the complicated forest structure of near natural forests in the temperate zone.This study was carried out in the Polish part of the Białowieża Forest (BF). The airborne laser scanner (ALS) and color-infrared (CIR) datasets were acquired for more than 60 000 ha. Field-based measurements were performed to provide reference data at the single tree level. We introduced a novel ITD method that is self-calibrated and uses a hierarchical analyses of the canopy height model.There were more than 20 000 000 of trees in first layer in BF above 7 m height. Trees visible from above were divided into coniferous, deciduous and mixed trees that were then matched with an accuracy of 85 %, 85 % and 75 %, respectively. Compared to existing methods, the proposed method is more flexible and achieves better results, especially for deciduous species. Before application of the presented method to other regions, the calibration based on the developed optimisation procedure is needed. 相似文献
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针对道路高精度地图采集中道路标识线快速分类提取问题,提出了一种根据几何结构特征对道路标识线进行自动匹配提取的方法。首先利用点云生成强度特征图像,并根据点云密度分布特征将强度特征图像划分为多个区域;然后对每个区域进行二值化处理;最后考虑路面标识线缺失情况,设计了一种多约束条件的几何特征语义识别模型,实现了不同标识线类型的分类。对车载移动测量系统获取的多段道路点云数据进行实验,结果表明,该方法的道路标识线提取准确率和识别准确率分别为90.01%和82.83%,总体精度达到86.27%。 相似文献