Bias in land cover change estimates due to misregistration

Land cover change may be overestimated due to positional error in multi-temporal images. To assess the potential magnitude of this bias, we introduced random positional error to identical classified images and then subtracted them. False land cover change ranged from less than 5% for a 5-class AVHRR classification, to more than 33% for a 20-class Landsat TM classification. The potential for false change was higher with more classes. However, false change could not be reliably estimated simply by number of classes, since false change varied significantly by simulation trial when class size remained constant. Registration model root mean squared (rms) error may underestimate the actual image co-registration asccuracy. In simulations with 5 to 50 ground control locations, the mean model rms error was always less than the actual population rms error. The model rms error was especially unreliable when small sample sizes were used to develop second order rectification models. We introduce a bootstrap resampling method to estimate false land cover change due to positional error. Although the bootstrap estimates were unbiased, the precision of the estimates may be too low to be of practical value in some land cover change applications.     

Interpreting the change detection error matrix

Two different matrices are commonly reported in assessment of change detection accuracy: (1) single date error matrices and (2) binary change/no change error matrices. The third, less common form of reporting, is the transition error matrix. This paper discuses the relation between these matrices. First, it is shown that the transition error matrix implicitly measures temporal correlation in classification errors. Based on two assumptions (no correlation, maximum correlation), the single date error matrices can be used to obtain a most pessimistic and most optimistic estimate of the transition accuracy. Next, it is shown that the change/no change error matrix does not quantify certain classification errors. It is shown that change/no change error matrix can be used complementary to the full transition error matrix in efforts to improve transition detection accuracy. Despite its advantages, the transition error matrix is only very rarely reported, while it is of interest to all those interested in the accuracy of transitions (from-to) in change detection.     

Daytime fire detection using airborne hyperspectral data

The shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, can include significant emitted radiance from fire. There have been relatively few evaluations of the utility of shortwave infrared remote sensing data, and in particular hyperspectral remote sensing data, for fire detection. We used an Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) scene acquired over the 2003 Simi Fire to identify the hyperspectral index that was able to most accurately detect pixels containing fire. All AVIRIS band combinations were used to calculate normalized difference indices, and kappa was used to compare classification ability of these indices for three different fire temperature ranges. The most accurate index was named the Hyperspectral Fire Detection Index (HFDI). The HFDI uses shortwave infrared bands centered at 2061 and 2429 nm. These bands are sensitive to atmospheric attenuation, so the impacts of variable elevation, solar zenith angle, and atmospheric water vapor concentration on HFDI were assessed using radiative transfer modeling. While varying these conditions did affect HFDI values, relative differences between background HFDI and HFDI for 1% fire pixel coverage were maintained. HFDI is most appropriate for detection of flaming combustion, and may miss lower temperature smoldering combustion at low percent pixel coverage due to low emitted radiance in the shortwave infrared. HFDI, two previously proposed hyperspectral fire detection indices, and a broadband shortwave infrared-based fire detection index were applied to AVIRIS scenes acquired over the 2007 Zaca Fire and 2008 Indians Fire. A qualitative comparison of the indices demonstrated that HFDI provides improved detection of fire with less variability in background index values.     

Using data mining technique to enhance tax evasion detection performance

Currently, tax authorities face the challenge of identifying and collecting from businesses that have successfully evaded paying the proper taxes. In solving the problem of tax evaders, tax authorities are equipped with limited resources and traditional tax auditing strategies that are time-consuming and tedious. These continued practices have resulted in the loss of a substantial amount of tax revenue for the government. The objective of the current study is to apply a data mining technique to enhance tax evasion detection performance. Using a data mining technique, a screening framework is developed to filter possible non-compliant value-added tax (VAT) reports that may be subject to further auditing. The results show that the proposed data mining technique truly enhances the detection of tax evasion, and therefore can be employed to effectively reduce or minimize losses from VAT evasion.     

Combining optical and acoustic data to enhance the detection of Caribbean forereef habitats

Maps of coral reef habitats are fundamental tools for reef management, and high map accuracy is desirable to support appropriate decisions, such as the stratification of marine reserves by habitat class. While satellite sensors have been used to map different reef communities, the accuracy of these maps tends to be low (overall accuracy < 50%) and optical airborne methods with high spectral resolution have, to date, been the most effective (if expensive) means of achieving higher accuracy. A potential means of compensating for the low spectral and radiometric resolution of optical satellite data, which is a major cause of its poor performance, is to combine satellite data with acoustic remote sensing. This study quantified the benefit of the synergy between optical satellite data (IKONOS) and acoustic (RoxAnn) sensors. The addition of acoustic data provided three new data axes for discriminating habitats: seabed roughness (E1), reef depth (z) and the depth correction of satellite spectral data to uniform depth. Seabed hardness (E2) was not an informative channel in our study. The use of z to conduct the water-column correction of the optical bands to uniform depth is a potential improvement over applying the depth-invariant index approach to optical data in the absence of ancillary information on depth. Habitat maps of the forereef of Glovers Atoll (Belize, Central America) were created using k-means unsupervised classification on eleven different treatment images constructed from various combinations of optical and acoustic data layers. The maximum benefit of data synergy was achieved by depth correcting the optical bands. The accuracy of maps based on the depth-invariant optical index was not enhanced when E1, E2 or z were added as separate layers but was enhanced when these three acoustic measures were added in concert. Data synergy can improve the accuracy of habitat maps and the availability of both data sets allows practitioners to take advantage of each techniques' additional strengths such as providing synoptic continuous imagery for education and general management planning (in the case of optical imagery) and maps of reef rugosity (in the case of acoustic data).     

Evaluation of sphericity error from coordinate measurement data using computational geometric techniques

The measurement data of a spherical component can be obtained from inspection devices such as coordinate measuring machines (CMMs). The sphericity error is evaluated from such coordinate data based on the minimum circumscribed sphere, the maximum inscribed sphere and minimum zone spheres. Appropriate methods based on the computational geometry have been developed to establish these assessment spheres. The present methods start with construction of 3-D hulls. The 3-D convex outer hull is established using the computational geometric concept presently available. For establishing a 3-D inner hull, a new heuristic method is suggested in this paper. A new concept of 3-D equidistant (ED) line is introduced in the present method. Based on this concept, the authors have constructed 3-D farthest and nearest equidistant diagrams for establishing the assessment spheres. Algorithms proposed in the present work are implemented and validated with the simulated data and the data available in the literature.     

DEM generation and error analysis using the first Chinese airborne dual-antenna interferometric SAR data

Terrain survey with traditional photogrammetry is often difficult in western China, such as Qingzang tableland at an average height of 5000 m above sea level and the southwest China area with cloudy weather. To resolve western terrain mapping, the first Chinese single-pass airborne Interferometric Synthetic Aperture Radar (InSAR) system was successfully developed by the Institute of Electronics, Chinese Academy of Sciences (IECAS) in 2004. The main objective of this article is to examine and evaluate the performance of the airborne SAR system through interferometric processing and error analysis. First, the article describes how high-precision digital elevation models (DEMs) are derived from the airborne dual-antenna (single-pass) InSAR data. In order to improve the precision, the antenna eccentricity correction and parameter calibration with the least square method (LSM) are proposed. Based on the airborne dual-antenna InSAR bore-sight model, this article summarizes the primary factors that influence the accuracy of DEMs in data processing, and analyses the errors induced by these factors. Then, the global positioning system (GPS)/inertial measurement unit (IMU) data, acquired and stored by the position and orientation system (POS), is used for analysing the quantitative relationships among the platform height, baseline length, baseline angle, look angle and DEM error. The experimental data used are airborne dual-antenna X-band InSAR data, and the measured ground control points (GCPs) are used to validate the accuracy of the DEM. The evaluation results in terms of the standard deviation (SD) and the average mean error (AME) are derived by comparing the reconstructed InSAR DEM with the reference GCPs. The AMEs of the X-direction, the Y-direction and the height are up to 2.078, 9.149 and 1.763 m, respectively. The SDs of the X-direction, the Y-direction and the height are up to ±1.379, ±0.764 and ±1.086 m, respectively. These results agree with the previously calculated quantitative errors. The error value of the Y-direction seems too large, a possible result of system errors. In general, the airborne dual-antenna InSAR system initially meets the requirements of 1:50 000 terrain mapping in western China.     

基于场景检测的自适应Intra帧差错掩盖算法

为了减小视频传输中的错误对解码端重建视频质量的影响,提出了一种基于场景检测的自适应Intra帧差错掩盖算法。该算法利用H.264/AVC的帧内预测模式分布差异检测Intra帧的场景切换,对非场景切换的Intra帧进行空时域掩盖的失真度估算,在此基础上进行自适应的空时域掩盖。实验结果表明,该算法充分利用了Intra帧的时域相关性,与已有的Intra帧差错掩盖算法比较,该算法恢复的视频序列平均峰值信噪比提高了1~2 dB,具有更好的差错掩盖效果。     

Anomaly detection in monitoring sensor data for preventive maintenance

Today, many industrial companies must face problems raised by maintenance. In particular, the anomaly detection problem is probably one of the most challenging. In this paper we focus on the railway maintenance task and propose to automatically detect anomalies in order to predict in advance potential failures. We first address the problem of characterizing normal behavior. In order to extract interesting patterns, we have developed a method to take into account the contextual criteria associated to railway data (itinerary, weather conditions, etc.). We then measure the compliance of new data, according to extracted knowledge, and provide information about the seriousness and the exact localization of a detected anomaly.     

A geometric approach to edge detection

This paper describes edge detection as a composition of four steps: conditioning, feature extraction, blending, and scaling. We examine the role of geometry in determining good features for edge detection and in setting parameters for functions to blend the features. We find that: (1) statistical features such as the range and standard deviation of window intensities can be as effective as more traditional features such as estimates of digital gradients; (2) blending functions that are roughly concave near the origin of feature space ran provide visually better edge images than traditional choices such as the city-block and Euclidean norms; (3) geometric considerations ran be used to specify the parameters of generalized logistic functions and Takagi-Sugeno input-output systems that yield a rich variety of edge images; and (4) understanding the geometry of the feature extraction and blending functions is the key to using models based on computational learning algorithms such as neural networks and fuzzy systems for edge detection. Edge images derived from a digitized mammogram are given to illustrate various facets of our approach     

Mapping simulated error due to terrain slope in airborne lidar observations

Quantification of geo-location error in light detection and ranging (lidar) observations is typically limited to empirical assessments, commonly quantified as the fundamental vertical accuracy (FVA). Methodological recommendations indicate that validation observations used for quantifying the FVA should not be collected in sloped terrain; however, terrain slope has been shown to contribute to the lidar error budget. Therefore, users of lidar information generally do not have adequate information to characterize error in sloped conditions. This study proposes a novel geometric terrain-based error propagation algorithm for simulating error bounds of individual lidar observations in the presence of terrain slope. A steep, glacierized, alpine test site in the Canadian Rockies was used to evaluate the algorithm. Error simulations were modelled from the terrain-based error propagation algorithm as well as a pre-existing sensor hardware error propagation algorithm and validated with high-accuracy GPS observations. Simulated versus observed errors showed that terrain-based error simulations provided a reasonable ‘worst-case scenario’ simulation of potential error and were superior to hardware-only simulated errors. Results were separated into three individual flight lines, and terrain-based error simulations were greater than the observed errors in 82%, 89%, and 100% of the tested points in each respective flight line, or 90%, overall. This contrasted hardware-only error simulations, which were greater than observed errors in 32%, 42%, and 84% of tested points in each respective flight line, or 50% overall. This work provides a comprehensive understanding of the distribution of errors within lidar point clouds over complex terrain types and provides a new methodology for propagating lidar observational errors into derived products.     

Automatic outlier detection for time series: an application to sensor data

In this article we consider the problem of detecting unusual values or outliers from time series data where the process by which the data are created is difficult to model. The main consideration is the fact that data closer in time are more correlated to each other than those farther apart. We propose two variations of a method that uses the median from a neighborhood of a data point and a threshold value to compare the difference between the median and the observed data value. Both variations of the method are fast and can be used for data streams that occur in quick succession such as sensor data on an airplane. Martin Meckesheimer has been a member of the Applied Statistics Group at Phantom Works, Boeing since 2001. He received a Bachelor of Science Degree in Industrial Engineering from the University of Pittsburgh in 1997, and a Master's Degree in Industrial and Systems Engineering from Ecole Centrale Paris in 1999. Martin earned a Doctorate in Industrial Engineering from The Pennsylvania State University in August 2001, as a student of Professor Russell R. Barton and Dr. Timothy W. Simpson. His primary research interests are in the areas of design of experiments and surrogate modeling. Sabyasachi Basu received his Ph.D. is Statistics from the University of Wisconsin at Madison in 1990. Since his Ph.D., he has worked in both academia and in industry. He has taught and guided Ph.D. students in the Department of Statistics at the Southern Methodist University. He has also worked as a senior marketing statistician at the J. C. Penney Company. Dr. Basu is also an American Society of Quality certified Six Sigma Black Belt. He is currently an Associate Technical Fellow in Statistics and Data Mining at the Boeing Company. In this capacity, he works as a researcher and a technical consultant within Boeing for data mining, statistics and process improvements. He has published more than 20 papers and technical reports. He has also served as journal referee for several journals, organized conferences and been invited to present at conferences.     

数据流变化检测研究综述

数据流是一种动态数据,它在某种因素的驱动下可能会随时间发生变化,而这种变化往往隐含着现实世界的某种事件。如何及时、准确地发现数据流中的变化已成为数据流挖掘的一个研究热点,并且在实际中有非常广泛的应用。描述了数据流变化及变化检测的核心任务,归纳了变化检测的通用框架,分析评价了目前已知的数据流变化检测方法及其技术特点,最后展望了数据流变化检测技术的发展方向。     

数据更新中要素变化检测与匹配方法

在要素类之间缺乏同名实体关联关系的情况下,通过空间分析自动识别出当前要素的同名实体及它们之间的变化信息。在查询当前要素的候选匹配集时,设计了一种基于自定义空间拓扑关系的空间查询方法,缩小了的空间查询范围并减少了查询次数,提高了空间分析的效率;在确定当前要素的同名实体时,提出了基于权重的空间要素相似性计算模型,基于该模型有效地对复杂空间关系下的要素进行了匹配,提高了匹配的准确性。     

基于加权MMMD的软测量数据过失误差侦测

在基于聚类分析的软测量过失误差侦测中,针对不同变量对建模影响程度的不同,提出了基于加权MMMD聚类分析的过失误差侦测方法。该方法在聚类的相似度计算时,根据各辅助变量与主导变量的相关性,给每个变量加以不同的权值。在新的相似性度量基础上进行聚类,并侦测出过失误差。用该方法对丙烯浓度的软测量数据进行了过失误差侦测,并用剔除过失误差后的数据做了建模仿真。实验结果表明了新方法的有效性,并能提高软测量数据的整体质量。     

Hybrid online sensor error detection and functional redundancy for systems with time-varying parameters

Supervision and control systems rely on signals from sensors to receive information to monitor the operation of a system and adjust manipulated variables to achieve the control objective. However, sensor performance is often limited by their working conditions and sensors may also be subjected to interference by other devices. Many different types of sensor errors such as outliers, missing values, drifts and corruption with noise may occur during process operation. A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model, which leverage the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. The system includes a nominal angle analysis (NAA) method to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in process operation. The performance of the system is illustrated with clinical data continuous glucose monitoring (CGM) sensors from people with type 1 diabetes. More than 50,000 CGM sensor errors were added to original CGM signals from 25 clinical experiments, then the performance of error detection and functional redundancy algorithms were analyzed. The results indicate that the proposed system can successfully detect most of the erroneous signals and substitute them with reasonable estimated values computed by functional redundancy system.     

A multigrid method for the estimation of geometric anisotropy in environmental data from sensor networks

This paper addresses the estimation of geometric anisotropy parameters from scattered spatial data that are obtained from environmental surveillance networks. Estimates of geometric anisotropy improve the accuracy of spatial interpolation procedures that aim to generate smooth maps for visualization of the data and for decision making purposes. The anisotropy parameters involve the orientation angle of the principal anisotropy axes and the anisotropy ratio (i.e., the ratio of the principal correlation lengths). The approach that we employ is based on the covariance Hessian identity (CHI) method, which links the mean gradient tensor with the Hessian matrix of the covariance function. We extend CHI to clustered CHI for application in data sets that include patches of extreme values and clusters of varying sampling density. We investigate the impact of CHI anisotropy estimation on the performance of spatial interpolation by ordinary kriging using a data set that involves both real background radioactivity measurements and a simulated release of a radioactive plume.     

国外机／舰载传感器的发展现状

机载传感器和舰载传感器在现代高技术军事装备信息化中占据非常重要的地位。介绍了国外机／舰载传感器的种类，综述了国外机／舰载传感器在航空、航天等领域的应用和研究现状。     

Land-cover change detection using multi-temporal MODIS NDVI data

Monitoring the locations and distributions of land-cover changes is important for establishing links between policy decisions, regulatory actions and subsequent land-use activities. Past studies incorporating two-date change detection using Landsat data have tended to be performance limited for applications in biologically complex systems. This study explored the use of 250 m multi-temporal MODIS NDVI 16-day composite data to provide an automated change detection and alarm capability on a 1 year time-step for the Albemarle-Pamlico Estuary System (APES) region of the US. Detection accuracy was assessed for 2002 at 88%, with a reasonable balance between change commission errors (21.9%), change omission errors (27.5%), and Kappa coefficient of 0.67. Annual change detection rates across the APES over the study period (2002-2005) were estimated at 0.7% per annum and varied from 0.4% (2003) to 0.9% (2004). Regional variations were also readily apparent ranging from 1.6% to 0.1% per annum for the tidal water and mountain ecological zones, respectfully. This research included the application of an automated protocol to first filter the MODIS NDVI data to remove poor (corrupted) data values and then estimate the missing data values using a discrete Fourier transformation technique to provide high-quality uninterrupted data to support the change detection analysis. The methods and results detailed in this article apply only to non-agricultural areas. Additional limitations attributed to the coarse resolution of the NDVI data included the overestimation of change area that necessitated the application of a change area correction factor.