Spatial degradation of satellite data

Abstract

Two aspects of spatial degradation of satellite data are examined. The first describes a technique for spatially degrading high-resolution satellite data to produce comparable data sets over a range of coarser resolutions. In this study seven spatial resolution data sets are produced from Landsat Multispectral Scanner (MSS) data resulting in spatial resolutions ranging from 79 m to 4 km applying a spatial filter designed to simulate sensor response. The simulation is demonstrated for part of the Superior National Forest, Minnesota. The second part of the paper examines spatial degradation of coarse resolution data to provide data compression for the production of global-scale data sets. The on-board sampling approach adopted by the National Oceanographic and Atmospheric Administration (NOAA) to produce the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data from the 1 km Large Area Coverage (LAC) data, is compared to other sampling procedures. Six sampling procedures were compared for seven terrain types. The GAC sampling procedure provided a relatively poor representation of the 1 km data, particularly for heterogeneous areas. Coefficients of determination for the GAC sampling compared to the original data ranged from 0.49?0.76. Sampling procedures incorporating averaging resulted in a decrease in the variance as compared with the original data. Sampling procedures adopting single-value selection had higher variances and produced data values directly comparable with those from the original data. Sampling scheme design should consider data fidelity requirements as well as the engineering constraints of on-board processing.     

Development of expert systems for stream sampling in mineral processing plants

Stream sampling is essential for the performance assessment of a mineral processing plant. This process generates errors that are caused by stream material heterogeneity and incorrect cutter features. To control the sampling process efficiently, it is very important to evaluate and minimize the sampling errors. The objective of this paper is to introduce two expert systems for stream sampling in mineral processing plants. The first one is intended to inspect the correctness of sampling operations. It is called Sampling Correctness Inspector (SCI). The second one is destined for the evaluation of sampling errors. It is named Sampling Error Evaluator (SEE). These expert systems are validated successfully.     

山地地表覆被野外采样系统研究及应用

野外样点数据是土地覆被遥感制图中训练样本和精度检验样本的重要来源.在山区地表覆被野外样点采集过程中,受采样距离以及河流、地形等的阻挡,大量样点难以直接到达,影响了山地地表覆被野外样点的代表性.为了获取高质量的山区地表覆被野外样点,研发一款高精度、远距离、不接触、自动化采样的山地地表覆被信息野外采样系统显得十分必要.以移动式客户端为工作平台,基于Android操作系统和ArcGISAPIforAndroid设计了一套山地地表覆被野外采样系统.该系统提供了单点和双点交会采样两种模式,用户可以根据地表覆被距离选择不同模式自动化获取采样地物的地理坐标、地表覆被类型信息、图像信息和用户定制的其他属性信息.经野外实地验证,该系统可以获取肉眼可辨的不同距离的山地地表覆被样点,提高了地表覆被野外采样效率.获取的可见远距离地表覆被目标的坐标距离误差在３０m以内,满足３０m尺度土地覆被产品生产对样点定位精度的要求.     

基于支持向量机的多姿态人脸特征定位

提出一种多姿态人脸特征定位方法,在Adaboost定位的人脸区域中划分眼、鼻和嘴的搜索区域,利用眉眼和鼻嘴整体特征,通过大规模多姿态五官样本训练的支持向量机在搜索区域中确定候选眼、鼻及嘴区域。对候选眼、鼻及嘴区域进行筛选与合并以确定最佳位置,实现多姿态人脸上五官的准确定位。实验结果表明,该方法具有较好的精确性和鲁棒性,能适应复杂背景下表情变化的多姿态人脸上的眼、鼻及嘴的定位。     

Optimal field sampling for targeting minerals using hyperspectral data

This paper presents a statistical method for deriving optimal spatial sampling schemes. It focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF) classification techniques were applied to obtain rule mineral images. Each pixel in these rule images represents the similarity between the corresponding pixel in the hyperspectral image to a reference spectrum. The rule images provide weights that are utilized in objective functions of the sampling schemes which are optimized through a process of simulated annealing. A HyMAP 126-channel airborne hyperspectral data acquired in 2003 over the Rodalquilar area in Spain serves as an application to target those pixels with the highest likelihood of occurrence of a specific mineral and as a collection the location of these sampling points selected represent the distribution of that particular mineral. In this area, alunite being a predominant mineral in the alteration zones was chosen as the target mineral. Three weight functions are defined to intensively sample areas where a high probability and abundance of alunite occurs. Weight function I uses binary weights derived from the SAM classification image, leading to an even distribution of sampling points over the region of interest. Weight function II uses scaled weights derived from the SAM rule image. Sample points are arranged more intensely in areas of abundance of alunite. Weight function III combines information from several different rule image classifications. Sampling points are distributed more intensely in regions of high probable alunite as classified by both SAM and SFF, thus representing the purest of pixels. This method leads to an efficient distribution of sample points, on the basis of a user-defined objective.     

Evolutionary Sampling and Software Quality Modeling of High-Assurance Systems

Software quality modeling for high-assurance systems, such as safety-critical systems, is adversely affected by the skewed distribution of fault-prone program modules. This sparsity of defect occurrence within the software system impedes training and performance of software quality estimation models. Data sampling approaches presented in data mining and machine learning literature can be used to address the imbalance problem. We present a novel genetic algorithm-based data sampling method, named Evolutionary Sampling, as a solution to improving software quality modeling for high-assurance systems. The proposed solution is compared with multiple existing data sampling techniques, including random undersampling, one-sided selection, Wilson's editing, random oversampling, cluster-based oversampling, Synthetic Minority Oversampling Technique (SMOTE), and Borderline-SMOTE. This paper involves case studies of two real-world software systems and builds C4.5- and RIPPER-based software quality models both before and after applying a given data sampling technique. It is empirically shown that Evolutionary Sampling improves performance of software quality models for high-assurance systems and is significantly better than most existing data sampling techniques.      

LPC1768与AD7656带时标采样系统设计

以微控制器LPC1768为核心控制AD7656的采样电路,实现了电力系统监测数据带上准确时间标记的设计方案。系统采用LPC1768片内资源SSP0控制AD7656进行采样,并使用片内资源RTC,以获得带有实时时间标示的采样数据。带时标采样系统在工业实时监测系统中有良好的应用前景。     

自动售货机IC卡收费模块的设计与应用

GPRS网络通讯技术和智能IC卡应用技术的结合,应用于自动售货机无线网络管理系统的构建。模块由射频卡读写子模块和GPRS通讯子模块两部分组成。采用MDB总线与自动售货机控制器通讯。实际应用证实了其可行性,在自动售货行业有广泛的应用前景。     

A method and fortran program for quantitative sampling in paleontology

The Unit Sampling Method is a binomial sampling method applicable to the study of fauna preserved in rocks too well cemented to be disaggregated. Preliminary estimates of the probability of detecting each group in a single sampling unit can be converted to estimates of the group's volumetric abundance by means of correction curves obtained by a computer simulation technique. This paper describes the technique and gives the FORTRAN program.     

Table‐driven Adaptive Importance Sampling

Monte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene. In this paper, we present Table‐driven Adaptive Importance Sampling (TAIS), a sampling technique that augments existing importance functions with tabular importance maps that direct sampling towards undersampled regions of path space. The importance maps are constructed lazily, relying on information gathered during the course of sampling. During sampling the importance maps act either in parallel with or as a preprocess to existing importance sampling methods. We show that our adaptive importance maps can be effective at reducing variance in a number of rendering situations.     

一种基于自适应采样优化的WSN定位算法

针对蒙特卡罗定位算法采样效率低和采样次数多等缺陷,在SOMCL算法的基础上提出一种基于自适应采样优化的定位算法LAASO。该算法采用锚盒子与预测区域进一步优化采样区域,通过采样区域的大小自适应确定样本数目,利用SOMCL算法中的曲线拟合对样本权值进行优化。仿真测试表明,当速度变化率为25 m/s,且最大速度小于60 m/s时,相比MCL算法和SOMCL算法,LAASO算法定位精度分别提高了40%和36%,采样次数分别降低为20%和31.5%,且更适应于高速运行环境。     

Applying sampling to improve software inspections

The main objective of software inspections is to find faults in software documents. The benefits of inspections are reported from researchers as well as software organizations. However, inspections are time consuming and the resources may not be sufficient to inspect all documents. Sampling of documents in inspections provides a systematic solution to select what to be inspected in the case resources are not sufficient to inspect everything. The method presented in this paper uses sampling, inspection and resource scheduling to increase the efficiency of an inspection session. A pre-inspection phase is used in order to determine which documents need most inspection time, i.e. which documents contain most faults. Then, the main inspection is focused on these documents. We describe the sampling method and provide empirical evidence, which indicates that the method is appropriate to use. A Monte Carlo simulation is used to evaluate the proposed method and a case study using industrial data is used to validate the simulation model. Furthermore, we discuss the results and important future research in the area of sampling of software inspections.     

Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge

Over the last two decades, evolutionary algorithms (EAs) have become a popular approach for solving water resources optimization problems. However, the issue of low computational efficiency limits their application to large, realistic problems. This paper uses the optimal design of water distribution systems (WDSs) as an example to illustrate how the efficiency of genetic algorithms (GAs) can be improved by using heuristic domain knowledge in the sampling of the initial population. A new heuristic procedure called the Prescreened Heuristic Sampling Method (PHSM) is proposed and tested on seven WDS cases studies of varying size. The EPANet input files for these case studies are provided as supplementary material. The performance of the PHSM is compared with that of another heuristic sampling method and two non-heuristic sampling methods. The results show that PHSM clearly performs best overall, both in terms of computational efficiency and the ability to find near-optimal solutions. In addition, the relative advantage of using the PHSM increases with network size.     

Disturbance compensation in continuous time by sampled data control

This paper points out that whenever a class of disturbances can be compensated in continuous time, then it can also be compensated in continuous time by means of sampled data control with a suitably defined hold function. Sampling patterns can then be chosen essentially independent of the disturbances to be compensated so that, in principle, high-frequency disturbances can be compensated with low-frequency sampling     

网络测量中自适应数据采集方法

抽样方法广泛地应用于网络测量与其他领域对被测总体的指标进行估计.研究表明,多种网络指标呈现重尾分布或自相似的特征.这些特性为准确估计总体指标带来了诸多困难.但同时,对被测网络指标进行建模也有着重要的应用.然而,建立精确网络模型是困难的.从时间序列拟合角度出发,提出了一种基于拟合的自适应抽样方法,对被测指标进行基于测量的建模.工作主要体现在: (1) 采用分段线性函数对被测指标进行逼近,建立基于测量的模型; (2) 与常用的抽样方法相比,在相同的样本数情况下,由拟合模型对指标进行的估计更准确、更稳定;通过对两个测量记录的分析表明,在与常用抽样方法保持相同的拟合误差时,自适应抽样方法明显地减少了所需采集的样本数量; (3) 与其他概率抽样方法相比,自适应抽样最终抽取的样本数更稳定、更可靠,并给出了最终样本数的概率分布.     

基于FPGA的微小型导航计算机数据采集系统设计

提出了以高性能DSP为核心，由FPGA构成主要外部输入输出接口的导航计算机方案，并重点对由FPGA构成的ADC采样控制器进行了设计。处理器DSP只需通过EMIF接口访问FPGA中的FIFO即可进行数据接收。FPGA设计采用VHDL语言描述，并利用Quartus24．0对设计方案进行了仿真，仿真结果表明该设计是有效的。同时，本文也简述了DSP和FPGA接口的软硬件设计。     

An adaptive sampling strategy for Kriging metamodel based on Delaunay triangulation and TOPSIS

Metamodels have been widely used in engineering design and optimization. Sampling method plays an important role in the constructing of metamodels. This paper proposes an adaptive sampling strategy for Kriging metamodel based on Delaunay triangulation and TOPSIS (KMDT). In the proposed KMDT, Delaunay triangulation is employed to partition the design space according to current sample points. The area of each partitioned triangle is used to indicate the degree of dispersion of sample points, and the prediction error of Kriging metamodel at each triangle’s centroid is used to represent the local error of each triangle region. By calculating the weight of the area and prediction error for each triangle region using the entropy method and TOPSIS, the degree of dispersion of sample points and local errors of metamodel are taken into consideration to make a trade-off between global exploration and local exploitation during the sequential sampling process. As a demonstration, the proposed approach is compared to other three sampling methods using several numerical cases and the modeling of the aerodynamic coefficient for a three-dimensional aircraft. The result reveals that the proposed approach provides more accurate metamodel at the same simulation cost, which is very important in metamodel-based engineering design problems.     

压缩感知理论综述

信号采样是模拟的物理世界通向数字的信息世界之必备手。多年来,指导信号采样的理论基础一直是著名的Nyquist采样定理,但其产生的大量数据造成了存储空间的浪费。压缩感知（Compressed Sensing,CS）提出一种新的采样理论,它能够以远低于Nyquist采样速率采样信号。文章详述了压缩感知的基本理论,着重介绍了信号稀疏变换、观测矩阵设计和重构算法三个方面的最新进展,并从基础理论层面和实践层面详细探讨了现存的难点问题。     

VB实现简单随机抽样的DLL开发及应用

简单随机抽样是森林资源调查中最基本的方法,介绍简单随机抽样程序的开发及应用。     

有关正六边形点阵结构数字图像的研究

传统连续图像信号的采样过程采用的是矩形点阵结构。当连续图像信号的频带处于一个圆形区域之内时，正六边形点阵结构的采样密度比矩形点阵结构的采样密度要降低13．4％。但目前图像输入输出设备只支持矩形点阵结构的数字图像，所以首先讨论了满足Nyquist采样定理的正六边形点阵结构的采样矩阵(空间采样间隔)，及矩形点阵结构数字图像和正六边形点阵结构数字图像之间的转换。另一方面由于正六边形点阵结构的数字图像是不可分离信号，这给图像处理造成许多的不便。为此提出了一种基于可分离滤波器阵列的图像分解方法，降低了计算复杂度，得到类似矩形图像小波变换所得的多尺度分解结构，并给出重构图像的实验结果。