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基于表层卫星遥感观测的中深层海洋遥感对于了解海洋内部异常及其动力过程有重要意义。如何从现有的海洋表层遥感观测资料提取海洋内部关键动力环境信息场是具有挑战性的海洋遥感技术前沿。本文采用支持向量回归(SVR)方法,通过卫星遥感观测获取的多源海表参量(海表高度异常(SSHA)、海表温度异常(SSTA)、海表盐度异常(SSSA)和海表风场异常(SSWA)),选择最优参量输入组合,感知海洋次表层温度异常(STA),并用实测Argo数据作精度验证。结果表明SVR模型可准确估算全球尺度的STA(1000 m深度以浅);当SVR输入变量为2个(SSHA、SSTA)、3个(SSHA、SSTA、SSSA)、4个(SSHA、SSTA、SSSA、SSWA)时对应的平均均方差(MSE)分别为0.0090、0.0086、0.0087,平均决定系数(R2)分别为0.443、0.457、0.485。因此,除了SSHA和SSTA外,SSSA与SSWA的输入对SVR模型的估算有积极影响,有助于提高STA的估算精度。在全球增暖与减缓背景下,该研究可为从表层卫星遥感观测提取海洋内部热力异常信息研究提供重要技术支持,有利于拓展卫星对海观测范围。 相似文献
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Camps-Valls G. Munoz-Mari J. Gomez-Chova L. Richter K. Calpe-Maravilla J. 《Geoscience and Remote Sensing Letters, IEEE》2009,6(2):248-252
This letter presents two kernel-based methods for semisupervised regression. The methods rely on building a graph or hypergraph Laplacian with both the available labeled and unlabeled data, which is further used to deform the training kernel matrix. The deformed kernel is then used for support vector regression (SVR). Given the high computational burden involved, we present two alternative formulations based on the Nystrom method and the incomplete Cholesky factorization to achieve operational processing times. The semisupervised SVR algorithms are successfully tested in multiplatform leaf area index estimation and oceanic chlorophyll concentration prediction. Experiments are carried out with both multispectral and hyperspectral data, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in biophysical parameter retrieval. 相似文献
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为了使低成本MEMS陀螺仪数据的精度更高,本文提出了一种混合核函数支持向量回归（SVR）的MEMS陀螺仪随机误差预测模型,并通过粒子群优化（PSO）算法对模型参数和核函数参数进行优化;同时通过Allan方差法对SVR预测前后的MEMS陀螺仪随机误差数据进行分析。试验结果表明：混合核函数SVR对MEMS陀螺仪随机误差的预测准确度可达99.99%;当MEMS陀螺仪所处状态不同,但噪声特性相同时,可采用统一的SVR预测模型预测随机误差,研究结果为进一步用于MEMS陀螺仪的实时误差补偿中提供依据。 相似文献
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结合机载LiDAR数据,提出了一种改进的GLAS光斑点冠层高度地形校正模型,以校正后的GLAS光斑点作为输入样本,结合MODIS遥感影像,利用支持向量回归（SVR）的方法对研究区森林冠层高度进行分生态区估测,并利用野外调查数据和机载LiDAR冠层高度结果对估测结果进行验证。结果显示:研究区的坡度等级直接影响GLAS光斑点森林冠层高度估测精度,改进的地形校正模型可以较好的减小坡度对GLAS光斑点森林冠层高度估测的影响,模型精度RMSE稳定在3.25~3.48 m;不同生态分区的SVR模型估测精度较为稳定,其RMSE=6.41~7.56 m;与算数平均高相比,样地的Lorey's高与制图结果拟合最好,不同生态分区平均估测精度为80.3%。机载LiDAR冠层高度结果的验证平均精度为79.5%,和Lorey's高验证结果呈现较好的一致性。 相似文献
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Since coastal waters are one of the most vulnerable marine systems to environmental pollution, it is very important to operationally monitor coastal water quality. This study attempts to estimate two major water quality indicators, chlorophyll-a (chl-a) and suspended particulate matter (SPM) concentrations, in coastal environments on the west coast of South Korea using Geostationary Ocean Color Imager (GOCI) satellite data. Three machine learning approaches including random forest, Cubist, and support vector regression (SVR) were evaluated for coastal water quality estimation. In situ measurements (63 samples) collected during four days in 2011 and 2012 were used as reference data. Due to the limited number of samples, leave-one-out cross validation (CV) was used to assess the performance of the water quality estimation models. Results show that SVR outperformed the other two machine learning approaches, yielding calibration R2 of 0.91 and CV root-mean-squared-error (RMSE) of 1.74 mg/m3 (40.7%) for chl-a, and calibration R2 of 0.98 and CV RMSE of 11.42 g/m3 (63.1%) for SPM when using GOCI-derived radiance data. Relative importance of the predictor variables was examined. When GOCI-derived radiance data were used, the ratio of band 2 to band 4 and bands 6 and 5 were the most influential input variables in predicting chl-a and SPM concentrations, respectively. Hourly available GOCI images were useful to discuss spatiotemporal distributions of the water quality parameters with tidal phases in the west coast of Korea. 相似文献
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森林郁闭度是森林资源调查中的一个重要因子,对森林质量评价具有重要作用。随着人工智能技术和遥感技术的不断发展,研究如何利用深度学习有效协同不同空间覆盖能力的遥感数据实现区域森林郁闭度的估测具有重要意义。由此提出了一种协同应用高密度无人机激光雷达和高空间分辨率卫星遥感数据,对区域森林郁闭度进行定量估测的深度学习模型（UnetR）。对用于图像分类的Unet模型的损失函数进行改进,并在卷积层后加入批量归一化层,使其具有对连续变量进行定量估测的能力。与全卷积神经网络、随机森林和支持向量机回归模型进行对比实验。结果表明,UnetR模型的均方根误差较低,估测精度较高,为实现区域森林郁闭度遥感监测提供了一种人力成本低、自动化程度高的估测方法。 相似文献
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In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. 相似文献
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基于DMSP/OLS夜间灯光数据的居住区指数模型（HSI）广泛应用于区域尺度城市不透水面扩张监测。但是,在干旱区由于受到裸岩、沙漠、戈壁等低植被覆盖区干扰,HSI算法的精度和适应性受到了一定的影响。为解决这一问题,本文利用植被覆盖度作为调节系数,对灯光数据与植被指数进行动态调整,构建了适用于干旱区的城市植被调节不透水指数（VAISI）;然后采用SVR模型,通过机器学习的方法构建了城市不透率参考数据与VAISI之间的非线性关系模型,实现对干旱区区域尺度不透水面覆盖率估算;最后,对模型估算结果进行了精度验证和比较分析。试验结果表明：在干旱区,VAISI解决了由于灯光溢出问题及城市周边裸土等低植被覆盖等因素导致的城市周边裸土像元不透率估算过高问题,一定程度上提高了城市内部不透水面空间分布信息的表达能力,有效克服了非灯光区估算结果高于背景值的现象。平均相关系数R由0.69提升到0.79,RMSE由0.17降至0.14。 相似文献
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R. C. S. Taragi K. S. Bisht B. S. Sokhi 《Journal of the Indian Society of Remote Sensing》1994,22(3):131-138
For urban planning and decision making, exact information about population of a specific area is very important. In India, the population census is carried out after every decade by field survey. But to know the inter-censual population, field survey is not very appropriate method because it is not cost and time-effective. Remote sensing can help significantly in generating quick information about housing typology and housing stock, from which population data can be extracted with the help of limited field checks. Therefore, two techniques of estimation of population using remote sensing data have been employed for Saharanpur city as a case study and the results are very encouraging. 相似文献
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由于受到许多复杂因素影响,沉降监测过程中所得到的信号数据可能会含有噪声。Matlab中小波分析功能能够对信号数据中的噪声信号进行有效的分解,最后重构处理后的信号。通过实例对比,选用合适的去噪方法和去噪函数,对信号数据进行处理,最后能够得到更适合的最优估计。 相似文献
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杨赤中推估法是一种以二项系数加权游动平均为基础,按照迭代法几何滤波过程建立推估数学模型的最小二乘估值方法,它能够基于少量已知数据点取得好的插值效果。针对有限的降雨观测站点和分布不规则的雨量特征点数据,本文基于杨赤中滤波与推估法空间插值,以湖南省降雨观测站点数据为实例数据,实例研究了建立降雨估值数学模型和实现插值计算的过程。首先,按照逐步增大游动半径进行滤波建立杨赤中特征函数,选用负幂指数函数模型拟合特征函数,建立降雨估值数学模型;然后,设计了杨赤中空间插值计算的自动化流程,实现降雨观测变量规律性变化的最优估值,并构建出研究区连续分布的降雨空间信息;最后,将其与TRMM(Tropical Rainfall Measuring Mission)和DEM(Digital Elevation Model)数据作为协变量进行协同克里金插值的结果进行对比验证。结果表明:杨赤中滤波与推估法在不需要其他协变量的情况下,可以取得较好的插值模拟结果,证实了该方法针对少量且不规则分布的降雨观测点数据插值的可行性与有效性。 相似文献
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A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems 总被引：7，自引：0，他引：7
Dengsheng Lu Qi Chen Guangxing Wang Lijuan Liu Guiying Li Emilio Moran 《International Journal of Digital Earth》2016,9(1):63-105
Remote sensing-based methods of aboveground biomass (AGB) estimation in forest ecosystems have gained increased attention, and substantial research has been conducted in the past three decades. This paper provides a survey of current biomass estimation methods using remote sensing data and discusses four critical issues – collection of field-based biomass reference data, extraction and selection of suitable variables from remote sensing data, identification of proper algorithms to develop biomass estimation models, and uncertainty analysis to refine the estimation procedure. Additionally, we discuss the impacts of scales on biomass estimation performance and describe a general biomass estimation procedure. Although optical sensor and radar data have been primary sources for AGB estimation, data saturation is an important factor resulting in estimation uncertainty. LIght Detection and Ranging (lidar) can remove data saturation, but limited availability of lidar data prevents its extensive application. This literature survey has indicated the limitations of using single-sensor data for biomass estimation and the importance of integrating multi-sensor/scale remote sensing data to produce accurate estimates over large areas. More research is needed to extract a vertical vegetation structure (e.g. canopy height) from interferometry synthetic aperture radar (InSAR) or optical stereo images to incorporate it into horizontal structures (e.g. canopy cover) in biomass estimation modeling. 相似文献
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大气负荷响应计算误差估计模型 总被引：1，自引：0，他引：1
罗少聪 《武汉大学学报(信息科学版)》2001,26(3):217-221
用数理统计技术，建立格林函方法计算大气变化引起重力和形焦等效应的精度评定模型，并利用中国及其邻区的大气观,蛭大气误差常数和一个计算方案的精度估计，其结果与固体潮汐检测在不同气候条件下的响应情况相一致，该系统也可应用于格林函数积分形式的其它物理模型的误差估计。 相似文献
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里程计通常被用于辅助车载GNSS/INS组合导航系统,以解决当遇到高楼、密林、隧道等信号干扰和遮蔽严重情景时导致精度下降的问题,而里程计辅助需要获取准确的里程计杆臂和安装角。鉴于此,本文提出了一种基于预积分的IMU/ODO外参估计算法,使用由里程计观测和GNSS/INS组合导航解算得到的一段时间内的里程增量差异构建代价函数,通过非线性优化器进行标定参数求解。仿真与实际测试均表明了本文标定方法的有效性,里程计观测在经过标定外参补偿后,可为车载GNSS/INS组合导航系统提供厘米级的精度辅助。 相似文献
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核密度估计法支持下的网络空间POI点可视化与分析 总被引：7，自引：0，他引：7
城市空间POI点的分布模式、分布密度在基础设施规划、城市空间分析中具有重要意义,表达该特征的核密度法(kernel density estimation)由于顾及了地理学第一定律的区位影响,比其他密度表达方法(如样方密度、基于Voronoi图密度)占优。然而,传统的核密度计算方法往往基于二维延展的欧氏空间,忽略了城市网络空间中设施点的服务功能及相互联系发生于网络路径距离而非欧氏距离的事实。本研究针对该缺陷,给出了网络空间核密度计算模型,分析了核密度方法在置入网络结构中受多种约束条件的扩展模式,讨论了衰减阈值及高度极值对核密度特征表达的影响。通过实际多种POI点分布模式(随机型、稀疏型、区域密集型、线状密集型)下的核密度分析试验,讨论了POI基础设施在城市区域中的分布特征、影响因素、服务功能。 相似文献