Preprocessing of gravity gradients at the GOCE high-level processing facility

One of the products derived from the gravity field and steady-state ocean circulation explorer (GOCE) observations are the gravity gradients. These gravity gradients are provided in the gradiometer reference frame (GRF) and are calibrated in-flight using satellite shaking and star sensor data. To use these gravity gradients for application in Earth scienes and gravity field analysis, additional preprocessing needs to be done, including corrections for temporal gravity field signals to isolate the static gravity field part, screening for outliers, calibration by comparison with existing external gravity field information and error assessment. The temporal gravity gradient corrections consist of tidal and nontidal corrections. These are all generally below the gravity gradient error level, which is predicted to show a 1/f behaviour for low frequencies. In the outlier detection, the 1/f error is compensated for by subtracting a local median from the data, while the data error is assessed using the median absolute deviation. The local median acts as a high-pass filter and it is robust as is the median absolute deviation. Three different methods have been implemented for the calibration of the gravity gradients. All three methods use a high-pass filter to compensate for the 1/f gravity gradient error. The baseline method uses state-of-the-art global gravity field models and the most accurate results are obtained if star sensor misalignments are estimated along with the calibration parameters. A second calibration method uses GOCE GPS data to estimate a low-degree gravity field model as well as gravity gradient scale factors. Both methods allow to estimate gravity gradient scale factors down to the 10⁻³ level. The third calibration method uses high accurate terrestrial gravity data in selected regions to validate the gravity gradient scale factors, focussing on the measurement band. Gravity gradient scale factors may be estimated down to the 10⁻² level with this method.     

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

山东地磁台网受宁东高压直流输电干扰预处理质量分析

为进一步提高山东地磁台网受高压直流输电干扰的预处理质量,通过中国地震前兆数据处理系统中相对差值检测和预处理检测功能,对山东地磁台网多年来受宁东高压直流输电干扰预处理情况进行了总结分析。结果表明,经过数据预处理,宁东高压直流输电干扰基本被去除,但仍然存在高压直流输电干扰误判、起止时间错判、干扰幅度计量不精确等问题。针对上述问题,对数据进行复核和校对,应用"缓变台阶"功能重新处理,并加强对H、D分量干扰特征的研究。     

THE GEOMETRY OF MULTIVARIATE OBJECT PREPROCESSING

The geometric properties of three common object-preprocessing transformations(constant sum,orclosure;constant length,or normalization;and maximum value,or ratioing)are investigated.Anargument is made for using absolute values in the constant sum and maximum value transformations.In general,each transformation distorts the shape and dimensionality of patterns in the data:transformed data lie on(C-l)-dimensional surfaces in the original C-dimensional space.A data set thathas been closed by one of these transformations can be reopened if a vector containing the constant sums,constant lengths or maximum values of the original objects was retained.Transformed data sets may befreely interconverted among these three transformations without the loss of information.     

CPU/GPU near real-time preprocessing for ZY-3 satellite images: Relative radiometric correction,MTF compensation,and geocorrection

ZY-3 is the first high-accuracy civil stereo-mapping optical satellite of China. It greatly improves China’s optical satellite image resolution with a boom in data volume, calling for new challenges in processing real-time applications. On the other hand, using central processing unit (CPU)/graphic processing unit (GPU) to resolve data-intensive remote sensing problems becomes a hot issue. In this paper, we present an approach for CPU/GPU near real-time preprocessing of ZY-3 satellite images, focusing on three key processors: relative radiometric correction (RRC), modulation transfer function compensation (MTFC), and geocorrection (GC). First, basic GPU implementation issues are addressed to make the processors capable of processing with GPU. Second, three effective GPU specific optimizations are applied for further improvement of the GPU performance. Furthermore, to fully exploit the CPU’s computing horsepower within the system, a CPU/GPU workload distribution scheme is proposed, in which CPU undertakes partial computation to share the workloads of GPU. The experimental result shows that our approach achieved an overall 48.84-fold speedup ratio in ZY-3 nadir image preprocessing (the corresponding run time is 11.60 s for one image), which is capable of meeting the requirement of near real-time response to the applications that follow. In addition, with the supportability of IEEE 754–2008 floating-point standard in the Fermi type GPU, preprocessing ZY-3 images with our CPU/GPU processors could maintain the quality of image preprocess as done traditionally with CPU processors.     

高光谱数据处理及其在广西苗儿山地区的地质应用研究

本文以广西苗儿山地区机载成像细分红外高光谱数据为例,探讨了高光谱数据的特点及其处理方法。编制了一系列进行数据预处理的软件,进行了条带去除、波段间配准、航向压缩、镜向变换、辐射校正、正切校正、相对反射率转换等预处理,获得了高质量图像。在ＥＮＶＩ软件基础上建立了研究区野外实测光谱曲线库及分类子库,并对各子库进行了系统研究。同时,提出了正确的配准方案,对图像进行了精校正和镶嵌,取得了满意的镶嵌图像;在纯净像元提取、混合像元分解、匹配滤波、光谱特征拟合等方面进行了探索。最后,探讨了高光谱数据的地质应用潜力,尤其在硅化带识别方面取得了成功的效果,发现了一系列新的含铀硅化断裂带;预测了３片成矿有利地段,经验证,在其中两片地区找到了明显的铀偏高和矿化显示。     

龙岩台VS型垂直摆倾斜仪常见故障分析

通过在日常观测工作中,对龙岩台VS型垂直摆倾斜仪出现的常见故障进行分析,总结了该仪器的典型故障现象及图像表现形式,以利于观测人员识别常见仪器故障,对相关仪器的维护使用、观测数据的预处理和日常工作具有一定的参考意义。     

相山地区地面高精度磁测数据

本文针对相山火山岩盆地的弱磁异常受地表磁性不均的强烈干扰形成磁场突变点多、幅值高的特点，提出了一种数据预处理方法──剖面三点插值切割法，能有效地剔除单个突变点，使磁异常曲线圆滑，并能保持异常特征，在实际应用中具有一定效果。     

THE EFFECT OF MEAN CENTERING ON PREDICTION IN MULTIVARIATE CALIBRATION

Traditionally,one form of preprocessing in multivariate calibration methods such as principal componentregression and partial least squares is mean centering the independent variables(responses)and thedependent variables(concentrations).However,upon examination of the statistical issue of errorpropagation in multivariate calibration,it was found that mean centering is not advised for some datastructures.In this paper it is shown that for response data which(i)vary linearly with concentration,(ii)have no baseline(when there is a component with a non-zero response that does not change inconcentration)and(iii)have no closure in the concentrations(for each sample the concentrations of allcomponents add to a constant,e.g.100%)it is better not to mean center the calibration data.That is,the prediction errors as evaluated by a root mean square error statistic will be smaller for a model madewith the raw data than a model made with mean-centered data.With simulated data relativeimprovements ranging from 1% to 13% were observed depending on the amount of error in thecalibration concentrations and responses.     

THE EFFECT OF MISLABELED SAMPLES ON THE PERFORMANCE OF THE LINEAR LEARNING MACHINE

Over the past 15 years the linear learning machine has been applied to a large number of chemicalproblems.The learning machine approach is conceptually simple and does not require knowledge aboutthe statistical distribution of the data.However,there are problems associated with this approach.Oneproblem which has not been investigated is the influence of mislabeled samples on the positioning of thehyerplane in feature space.If a few samples in a data set are incorrectly tagged prior to training(i.e.thesamples are labeled as members of class 2 even though they are actually members of class 1),it is stilIpossible using the linear learning machine to achieve a classification success rate of 100% for the trainingset.However,unfavorable results will be obtained for the prediction set.The magnitude of this effect andits potential implications regarding the proper use of the linear learning machine are discussed.