基于Gabor纹理描述的遥感影像分割

本文引入了在不同影像层次上的Gabor纹理特征,采用分裂-合并加智能像素精致的方法,实现遥感影像的非监督分割。实验结果表明采用Gabor滤波器为基础的多分辨率分析来描述高分辨率遥感影像的纹理特征,可以明显地描述影像的高、低频特征,并且基于Gabor纹理特征进行遥感影像的分割是有效的;将本文方法的分割结果与经典的eCognition分割结果进行了对比试验,表明本文方法的分割结果较好。     

Pattern detection in airborne LiDAR data using Laplacian of Gaussian filter

Methods for feature detection in laser scanning data have been studied for decades ever since the emergence of the technology. However, it is still one of the unsolved problems in LiDAR data processing due to difficulty of texture and structure information extraction in unevenly sampled points. The paper analyzes the characteristics of Laplacian of Gaussian (LoG) Filter and its potential use for structure detection in LiDAR data. A feature detection method based on LoG filtering is presented and experimented on the unstructured points. The method filters the elevation value (namely, z coordinate value) of each point by convolution using LoG kernel within its local area and derives patterns suggesting the existence of certain types of ground objects/features. The experiments are carried on a point cloud dataset acquired from a neighborhood area. The results demonstrate patterns detected at different scales and the relationship between standard deviation that defines LoG kernel and neighborhood size, which specifies the local area that is analyzed.     

极低频电磁观测数据噪声处理方法研究

“十五”期间,在全国各地震台站共安装了约12套ELF极低频电磁观测仪器,从记录到的观测资料来看,所有台站均不同程度地受到工频50 Hz及其谐波的干扰,同时还有一些不必要的信息存在,因此,对ELF观测数据进行滤波处理显得尤其重要。该文用MATLAB编程软件设计了FIR数字滤波器,运用理论数据和ELF实测数据对滤波器的性能进行了验证,分析了ELF时间序列经过带通滤波和陷波处理后的效果,发现其对台站ELF观测数据的处理有一定的应用价值。     

一种改进的滤除探地雷达直耦波的算法

探地雷达具有高效、快速、无损、抗干扰能力强等优点,成为浅层勘探的有力工具.雷达探测的准确性与资料处理有重要关系,处理技术将直接影响探测结果的准确性和可信度,本文主要对探地雷达信号直耦波的滤波处理技术进行了研究.典型的规则噪声直耦波具有强相关、能量强、呈水平状、出现早等特点,传统的滤波方法是采用KL变换.KL变换是对记录道在整个时间轴上的分析,不具备局部时间范围内的分析能力,KL变换滤波在提高剖面横向分辨率的同时,在垂向上会出现干扰,降低垂向分辨率.如果在KL变换基础上结合小波分时分频的特点,可以很好地解决上述问题.因此针对滤除直耦波及水平波,本文提出了小波域KL变换法,经模型实验与实际应用验证了改进后的滤波不仅横向分辨率较KL变换有明显的提高,而且垂向上也克服了KL变换的缺点,没有产生干扰波.     

Noise filtering of full-gravity gradient tensor data

In oil and mineral exploration, gravity gradient tensor data include higher-frequency signals than gravity data, which can be used to delineate small-scale anomalies. However, full-tensor gradiometry (FTG) data are contaminated by high-frequency random noise. The separation of noise from high-frequency signals is one of the most challenging tasks in processing of gravity gradient tensor data. We first derive the Cartesian equations of gravity gradient tensors under the constraint of the Laplace equation and the expression for the gravitational potential, and then we use the Cartesian equations to fit the measured gradient tensor data by using optimal linear inversion and remove the noise from the measured data. Based on model tests, we confirm that not only this method removes the high-frequency random noise but also enhances the weak anomaly signals masked by the noise. Compared with traditional low-pass filtering methods, this method avoids removing noise by sacrificing resolution. Finally, we apply our method to real gravity gradient tensor data acquired by Bell Geospace for the Vinton Dome at the Texas-Louisiana border.     

Assimilating process context information of cellular automata into change detection for monitoring land use changes

This article presents a new method of assimilating process context information into change detection for monitoring land use changes. The accurate information about land use changes is important for implementing many global and regional environmental models. Two types of models have been independently developed to obtain such information, including change detection models (e.g. pixel-to-pixel comparison, post-classification comparison and object-based change analysis) and simulation models (e.g. cellular automata (CA) and agent-based modelling). These models may have limitations in capturing land use dynamics when used alone. In this study, the ensemble Kalman filter is used to obtain the best estimate of land use changes by combining remote-sensing observations with urban simulation. Urban simulation is able to provide process context information such as diffusion and coalescence of urban development. This type of complementary information is useful for improving the performance of change detection. Compared with traditional change detection models, this integrated model has the potential to improve the performance of change detection in terms of accuracies and landscape metrics. For example, the assimilating (MLC + CA) method can show improvement of the total accuracy and the kappa coefficient by 2.5–5.2% and 3.6–7.4%, respectively, in this study.     

Self-modifying CA model using dual ensemble Kalman filter for simulating urban land-use changes

There are many different methods to calibrate cellular automata (CA) models for better simulation results of urban land-use changes. However, few studies have been reported on combination of parameter update and error control using local data in CA calibration procedures. This paper presents a self-modifying CA model (SM-CA) that uses the dual ensemble Kalman filter (dual EnKF), which enables the CA model to simultaneously update model parameters and simulation results by merging observation data (local data). We applied the proposed model to simulate urban land-use changes in a 13-year period (1993–2005) in Dongguan City, a rapidly urbanizing region in south China. Simulation results indicate that this model yields better simulation results than the conventional logistic-regression CA and decision-tree CA models. For example, the validation is carried out using cross-tabulation matrix. The simulation results of SM-CA have allocation disagreement of 10.18%, 19.64%, and 30.03% in 1997, 2001, and 2005, respectively, which are 2.12%, 2.47%, and 6% lower than conventional logistic-regression CA models.     

一种新的模式倾向误差估计算法及其在ENSO模拟中的应用

气候模式是我们理解、模拟和预报气候演变的重要工具。然而即使是目前最先进的耦合模式,其模拟和预报与大气/海洋的真实状态相比,仍存在较大偏差,这是由于在模式的倾向方程中不可避免地存在系统性的误差(倾向误差)。因此,减小模式倾向误差对改进模式的模拟和预报效果具有重要意义。该研究首先发展了一种新的计算模式倾向误差的估计算法——基于局地集合变换卡尔曼滤波器(local ensemble transform kalman filter, LETKF)同化技术的倾向误差估计算法。在此基础上,将新发展的算法应用到Zebiak-Cane (ZC)模式,通过同化海表面温度异常(sea surface temperature anomaly, SSTA)数据,估计随时空变化的倾向误差,并使用计算得到的倾向误差订正模式,进行积分模拟。结果表明: (1)倾向误差和ZC模式的模拟偏差具有高度相关性; (2)订正后的模式改善了对厄尔尼诺-南方涛动(El Niño-Southern Oscillation, ENSO)的一些重要特征的模拟。这说明新发展的模式倾向误差估计算法十分有效且在ENSO模拟中具有较好的应用价值,此外,这种新的模式倾向误差估计算法,计算高效简便,可便捷地应用于各模式中,利于推广。     

海洋资料同化对气候季节-年际预测技巧及初始场的影响试验

集合卡尔曼滤波(Ensemble Kalman filter, EnKF)是一种国内外广泛使用的海洋资料同化方案, 用集合成员的状态集合表征模式的背景误差协方差, 结合观测误差协方差, 计算卡尔曼增益矩阵, 有效地将观测信息添加到模式初始场中。由于季节、年际预测很大程度上受到初始场的影响, 因此资料同化可以提高模式的预测性能。本文在NUIST-CFS1.0预测系统逐日SST nudging的初始化方案上, 利用EnKF在每个月末将全场(full field)海表温度(sea surface temperature, SST)、温盐廓线(in-situ temperature and salinity profiles, T-S profiles)以及卫星观测海平面高度异常(sea level anomalies, SLA)观测资料同化到模式初始场中, 对比分析了无海洋资料同化以及加入同化后初始场的区别、加入海洋资料同化后模式提前1~24个月预测性能的差异以及对于厄尔尼诺-南方涛动(El Niño-southern oscillation, ENSO)预测技巧的影响。结果表明, 加入海洋资料同化能有效地改进初始场, 并且呈现随深度增加初始场改进越显著的特征。加入同化后, 对全球SST、次表层海水温度的平均预测技巧均有一定的提高, 也表现出随深度增加预测技巧改进越明显的特征。但加入海洋资料同化后, 模式对ENSO的预测技巧有所下降, 可能是由于模式误差的存在, 使得同化后的预测初始场从接近观测的状态又逐渐恢复到与模式动力相匹配的状态, 加剧了赤道太平洋冷舌偏西、中东部偏暖的气候平均态漂移。     

利用Butterworth滤波器平滑加速度计的双天线GNSS/MEMS组合测姿

在双天线GNSS/MEMS组合测姿中,低精度、低成本的MEMS加速度计输出通常包含比较大的噪声,导致由加速度计调平计算得到的水平角观测值精度较低,影响组合测姿的精度和可靠性。不同于传统的时域降噪方法,从频域的角度出发,根据实际车载运动中加速度计输出具有因汽车固有结构产生的高频振动噪声的特点,利用滑动窗口的Butterworth低通滤波器平滑加速度计输出。动态车载实验结果表明,该滤波器降噪效果明显。与滤波前相比,水平角观测值精度提升了约4倍,达到(0.643°,0.546°)。在此基础上,滤波后组合测姿精度也从(0.124°,0.738°,0.532°)提升到(0.122°,0.074°,0.052°),其中水平角精度提升了大约一个数量级,滤波后的测姿结果更加准确可靠。