Log in

Relevant bibliographies by topics / Spatial outlier detection / Journal articles

To see the other types of publications on this topic, follow the link: Spatial outlier detection.

Journal articles on the topic 'Spatial outlier detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Spatial outlier detection.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

LU, CHANG-TIEN, DECHANG CHEN, and YUFENG KOU. "MULTIVARIATE SPATIAL OUTLIER DETECTION." International Journal on Artificial Intelligence Tools 13, no. 04 (2004): 801–11. http://dx.doi.org/10.1142/s021821300400182x.

Full text

Abstract:

A spatial outlier is a spatially referenced object whose non-spatial attribute values are significantly different from the values of its neighborhood. Identification of spatial outliers can lead to the discovery of unexpected, interesting, and useful spatial patterns for further analysis. Previous work in spatial outlier detection focuses on detecting spatial outliers with a single attribute. In the paper, we propose two approaches to discover spatial outliers with multiple attributes. We formulate the multi-attribute spatial outlier detection problem in a general way, provide two effective de

APA, Harvard, Vancouver, ISO, and other styles

2

Baba, Ali Mohammed, Habshah Midi, and Nur Haizum Abd Rahman. "Spatial Outlier Accommodation Using a Spatial Variance Shift Outlier Model." Mathematics 10, no. 17 (2022): 3182. http://dx.doi.org/10.3390/math10173182.

Full text

Abstract:

Outlier detection has been a long-debated subject among researchers due to its effect on model fitting. Spatial outlier detection has received considerable attention in the recent past. On the other hand, outlier accommodation, particularly in spatial applications, retains vital information about the model. It is pertinent to develop a method that is capable of accommodating detected spatial outliers in a fashion that retains vital information in the spatial models. In this paper, we formulate the variance shift outlier model (SVSOM) in the spatial regression as a robust spatial model using re

APA, Harvard, Vancouver, ISO, and other styles

3

Mohd Ali, Nur Fatihah, Ibrahim Mohamed, Rossita Mohamad Yunus, and Faridah Othman. "Spatial Functional Outlier Detection in Multivariate Spatial Functional Data." Sains Malaysiana 53, no. 6 (2024): 1463–76. http://dx.doi.org/10.17576/jsm-2024-5306-18.

Full text

Abstract:

Multivariate spatial functional data consists of multiple functions of time-dependent attributes observed at each spatial point. This study focuses on detecting spatial outliers in spatial functional data. Firstly, we develop a new method called Mahalanobis Distance Spatial Outlier (MDSO) to detect functional outliers in the data. The method introduces the multivariate functional Mahalanobis semi-distance and multivariate pairwise functional Mahalanobis semi-distance metrics based on the multivariate functional principal components analysis to calculate the dissimilarity between functions at e

APA, Harvard, Vancouver, ISO, and other styles

4

Janeja, Vandana P., and Vijayalakshmi Atluri. "Spatial outlier detection in heterogeneous neighborhoods." Intelligent Data Analysis 13, no. 1 (2009): 85–107. http://dx.doi.org/10.3233/ida-2009-0357.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Singh, Alok Kumar, and S. Lalitha. "A novel spatial outlier detection technique." Communications in Statistics - Theory and Methods 47, no. 1 (2017): 247–57. http://dx.doi.org/10.1080/03610926.2017.1301477.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Xin, Liu, Zhang Shaoliang, and Zheng Pulin. "Spatial Outlier Detection of CO2 Monitoring Data Based on Spatial Local Outlier Factor." Journal of Engineering Science and Technology Review 8, no. 5 (2015): 110–16. http://dx.doi.org/10.25103/jestr.085.15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Huda, Nur'ainul Miftahul, Utriweni Mukhaiyar, and Nurfitri Imro'ah. "AN ITERATIVE PROCEDURE FOR OUTLIER DETECTION IN GSTAR(1;1) MODEL." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 3 (2022): 975–84. http://dx.doi.org/10.30598/barekengvol16iss3pp975-984.

Full text

Abstract:

Outliers are observations that differ significantly from others that can affect the estimation results in the model and reduce the estimator's accuracy. To deal with outliers is to remove outliers from the data. However, sometimes important information is contained in the outlier, so eliminating outliers is a misinterpretation. There are two types of outliers in the time series model, Innovative Outlier (IO) and Additive Outlier (AO). In the GSTAR model, outliers and spatial and time correlations can also be detected. We introduce an iterative procedure for detecting outliers in the GSTAR mode

APA, Harvard, Vancouver, ISO, and other styles

8

Filzmoser, Peter, and Mariella Gregorich. "Multivariate Outlier Detection in Applied Data Analysis: Global, Local, Compositional and Cellwise Outliers." Mathematical Geosciences 52, no. 8 (2020): 1049–66. http://dx.doi.org/10.1007/s11004-020-09861-6.

Full text

Abstract:

AbstractOutliers are encountered in all practical situations of data analysis, regardless of the discipline of application. However, the term outlier is not uniformly defined across all these fields since the differentiation between regular and irregular behaviour is naturally embedded in the subject area under consideration. Generalized approaches for outlier identification have to be modified to allow the diligent search for potential outliers. Therefore, an overview of different techniques for multivariate outlier detection is presented within the scope of selected kinds of data frequently

APA, Harvard, Vancouver, ISO, and other styles

9

Matkan, A. A., M. Hajeb, B. Mirbagheri, S. Sadeghian, and M. Ahmadi. "SPATIAL ANALYSIS FOR OUTLIER REMOVAL FROM LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 187–90. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-187-2014.

Full text

Abstract:

Outlier detection in LiDAR point clouds is a necessary process before the subsequent modelling. So far, many studies have been done in order to remove the outliers from LiDAR data. Some of the existing algorithms require ancillary data such as topographic map, multiple laser returns or intensity data which may not be available, and some deal only with the single isolated outliers. This is an attempt to present an algorithm to remove both the single and cluster types of outliers, by exclusively use of the last return data. The outliers will be removed by spatial analyzing of LiDAR point clouds

APA, Harvard, Vancouver, ISO, and other styles

10

Yixin Chen, Xin Dang, Hanxiang Peng, H. L. Bart, and H. L. Bart. "Outlier Detection with the Kernelized Spatial Depth Function." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 2 (2009): 288–305. http://dx.doi.org/10.1109/tpami.2008.72.

Full text

APA, Harvard, Vancouver, ISO, and other styles

11

Alvera-Azcárate, A., D. Sirjacobs, A. Barth, and J. M. Beckers. "Outlier detection in satellite data using spatial coherence." Remote Sensing of Environment 119 (April 2012): 84–91. http://dx.doi.org/10.1016/j.rse.2011.12.009.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Dai, Xiaowen, Libin Jin, Anqi Shi, and Lei Shi. "Outlier detection and accommodation in general spatial models." Statistical Methods & Applications 25, no. 3 (2016): 453–75. http://dx.doi.org/10.1007/s10260-015-0348-1.

Full text

APA, Harvard, Vancouver, ISO, and other styles

13

K, Sajana O., and Sajesh T. A. "Detection of Multidimensional Outlier Using Multivariate Spatial Median." Journal of Computer and Mathematical Sciences 9, no. 12 (2018): 1875–81. http://dx.doi.org/10.29055/jcms/934.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Paik, Chunhyun, Yongjoo Chung, and Young Jin Kim. "Power Curve Modeling of Wind Turbines through Clustering-Based Outlier Elimination." Applied System Innovation 6, no. 2 (2023): 41. http://dx.doi.org/10.3390/asi6020041.

Full text

Abstract:

The estimation of power curve is the central task for efficient operation and prediction of wind power generation. It is often the case, however, that the actual data exhibit a great deal of variations in power output with respect to wind speed, and thus the power curve estimation necessitates the detection and proper treatment of outliers. This study proposes a novel procedure for outlier detection and elimination for estimating power curves of wind farms by employing clustering algorithms of vector quantization and density-based spatial clustering of applications with noise. Testing differen

APA, Harvard, Vancouver, ISO, and other styles

15

Zhang, Yun, Bin Yang, Xi Zhao, Shiqian Wu, Bin Luo, and Liangpei Zhang. "Outlier Detection by Energy Minimization in Quantized Residual Preference Space for Geometric Model Fitting." Electronics 13, no. 11 (2024): 2101. http://dx.doi.org/10.3390/electronics13112101.

Full text

Abstract:

Outliers significantly impact the accuracy of geometric model fitting. Previous approaches to handling outliers have involved threshold selection and scale estimation. However, many scale estimators assume that the inlier distribution follows a Gaussian model, which often does not accurately represent cases in geometric model fitting. Outliers, defined as points with large residuals to all true models, exhibit similar characteristics to high values in quantized residual preferences, thus causing outliers to cluster away from inliers in quantized residual preference space. In this paper, we lev

APA, Harvard, Vancouver, ISO, and other styles

16

HU, TIANMIN, and SAM YUAN SUNG. "FINDING OUTLIERS AT MULTIPLE SCALES." International Journal of Information Technology & Decision Making 04, no. 02 (2005): 251–62. http://dx.doi.org/10.1142/s0219622005001507.

Full text

Abstract:

Outlier detection targets those exceptional data whose pattern is rare and lie in low density regions. In this paper, under the assumption of complete spatial randomness inside clusters, we propose an MDV (Multi-scale Deviation of the Volume) approach to identifying outliers. In addition to assigning an outlier score for each object, it directly outputs a crisp outlier set. It also offers a plot showing the data structure in every object's vicinity, which is useful in explaining why it may be outlying. Finally, the effectiveness of MDV is demonstrated with both artificial and real datasets.

APA, Harvard, Vancouver, ISO, and other styles

17

Mohd Ali, Nur Fatihah, Rossita Mohamad Yunus, Ibrahim Mohamed, and Faridah Othman. "Improved Spatial Outlier Detection Method within a River Network." Sains Malaysiana 51, no. 3 (2022): 911–27. http://dx.doi.org/10.17576/jsm-2022-5103-24.

Full text

Abstract:

A spatial outlier refers to the observation whose non-spatial attribute values are significantly different from those of its neighbors. Such observations can also be found in water quality data at monitoring stations within a river network. However, existing spatial outlier detection procedures based on distance measures such as the Euclidean distance between monitoring stations do not take into account the river network topology. In general, water quality levels in lower streams will be affected by the flow from the upper streams. Similarly, the water quality at some tributaries may have litt

APA, Harvard, Vancouver, ISO, and other styles

18

Waller, John. "Outlier Detection at GBIF Using DBSCAN." Biodiversity Information Science and Standards 4 (October 8, 2020): e59412. https://doi.org/10.3897/biss.4.59412.

Full text

Abstract:

Geographic outliers at GBIF (Global Biodiversity Information Facility) are a known problem. Outliers can be errors, coordinates with high uncertainty, or simply occurrences from an undersampled region. Often in data cleaning pipelines, outliers are removed (even if they are legitimate points) because the researcher does not have time to verify each record one-by-one. Outlier points are usually occurrences that need attention. Currently, there is no outlier detection implemented at GBIF and it is up to the user to flag outliers themselves.DBSCAN (a density-based algorithm for discovering cluste

APA, Harvard, Vancouver, ISO, and other styles

19

LU, CHANG-TIEN, RAIMUNDO F. DOS SANTOS, XUTONG LIU, and YUFENG KOU. "A GRAPH-BASED APPROACH TO DETECT ABNORMAL SPATIAL POINTS AND REGIONS." International Journal on Artificial Intelligence Tools 20, no. 04 (2011): 721–51. http://dx.doi.org/10.1142/s0218213011000309.

Full text

Abstract:

Spatial outliers are the spatial objects whose nonspatial attribute values are quite different from those of their spatial neighbors. Identification of spatial outliers is an important task for data mining researchers and geographers. A number of algorithms have been developed to detect spatial anomalies in meteorological images, transportation systems, and contagious disease data. In this paper, we propose a set of graph-based algorithms to identify spatial outliers. Our method first constructs a graph based on k-nearest neighbor relationship in spatial domain, assigns the differences of nons

APA, Harvard, Vancouver, ISO, and other styles

20

Kamal, Sahar, Rabie Ramadan, and Fawzy El-Refai. "Smart outlier detection of wireless sensor network." Facta universitatis - series: Electronics and Energetics 29, no. 3 (2016): 383–93. http://dx.doi.org/10.2298/fuee1603383k.

Full text

Abstract:

Data sets collected from wireless sensor networks (WSN) are usually considered unreliable and subject to errors due to limited sensor capabilities and hard environment resulting in a subset of the sensors data called outlier data. This paper proposes a technique to detect outlier data base on spatial-temporal similarity among data collected by geographically distributed sensors. The proposed technique is able to identify an abnormal subset of data collected by sensor node as outlier data. Moreover, the proposed technique is able to classify this abnormal observation, an error data set or event

APA, Harvard, Vancouver, ISO, and other styles

21

Protopapadakis, Eftychios, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Dimitrios Dres, and Matthaios Bimpas. "Stacked Autoencoders for Outlier Detection in Over-the-Horizon Radar Signals." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/5891417.

Full text

Abstract:

Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as th

APA, Harvard, Vancouver, ISO, and other styles

22

Nguyen, Hoc Thai, and Nguyen Huu Thai. "Temporal and spatial outlier detection in wireless sensor networks." ETRI Journal 41, no. 4 (2019): 437–51. http://dx.doi.org/10.4218/etrij.2018-0261.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

M.Dimble, Nita, and Bharat Tidke. "A Framework for Outlier Detection in Geographic Spatial Data." International Journal in Foundations of Computer Science & Technology 5, no. 2 (2015): 59–67. http://dx.doi.org/10.5121/ijfcst.2015.5206.

Full text

APA, Harvard, Vancouver, ISO, and other styles

24

Wagner, Helene H., Mariana Chávez-Pesqueira, and Brenna R. Forester. "Spatial detection of outlier loci with Moran eigenvector maps." Molecular Ecology Resources 17, no. 6 (2017): 1122–35. http://dx.doi.org/10.1111/1755-0998.12653.

Full text

APA, Harvard, Vancouver, ISO, and other styles

25

Harris, Paul, Chris Brunsdon, Martin Charlton, Steve Juggins, and Annemarie Clarke. "Multivariate Spatial Outlier Detection Using Robust Geographically Weighted Methods." Mathematical Geosciences 46, no. 1 (2013): 1–31. http://dx.doi.org/10.1007/s11004-013-9491-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Mao, Jiali, Jiaye Liu, Cheqing Jin, and Aoying Zhou. "Feature Grouping–based Trajectory Outlier Detection over Distributed Streams." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (2021): 1–23. http://dx.doi.org/10.1145/3444753.

Full text

Abstract:

Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we f

APA, Harvard, Vancouver, ISO, and other styles

27

Hou, Z., Y. Chen, K. Tan, and P. Du. "NOVEL HYPERSPECTRAL ANOMALY DETECTION METHODS BASED ON UNSUPERVISED NEAREST REGULARIZED SUBSPACE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 539–46. http://dx.doi.org/10.5194/isprs-archives-xlii-3-539-2018.

Full text

Abstract:

Anomaly detection has been of great interest in hyperspectral imagery analysis. Most conventional anomaly detectors merely take advantage of spectral and spatial information within neighboring pixels. In this paper, two methods of Unsupervised Nearest Regularized Subspace-based with Outlier Removal Anomaly Detector (UNRSORAD) and Local Summation UNRSORAD (LSUNRSORAD) are proposed, which are based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. Using a dual window, an approximation of each testing pixel is a rep

APA, Harvard, Vancouver, ISO, and other styles

28

Pusdiktasari, Zerlita Fahdha, Rahma Fitriani, and Eni Sumarminingsih. "An Improved Weighted Median Algorithm for Spatial Outliers Detection." ComTech: Computer, Mathematics and Engineering Applications 13, no. 2 (2022): 111–21. http://dx.doi.org/10.21512/comtech.v13i2.7821.

Full text

Abstract:

A spatial outlier is an object that significantly deviates from its surrounding neighbors. The median algorithm is one of the spatial outlier methods, which is robust. However, it assumes that all spatial objects have the same characteristics. Meanwhile, the Average Difference Algorithm (AvgDiff) has accommodated the differences in spatial characteristics, but it does not use statistical tests to determine the status of an object, whether it is an outlier or not. The research developed an improved version of the median algorithm and AvgDiff, called the Weighted Median Algorithm (WMA) which com

APA, Harvard, Vancouver, ISO, and other styles

29

Puchhammer, Patricia, Charmee Kalubowila, Lorena Braus, Solveig Pospiech, Pertti Sarala, and Peter Filzmoser. "A performance study of local outlier detection methods for mineral exploration with geochemical compositional data." Journal of Geochemical Exploration 258 (January 9, 2024): 107392. https://doi.org/10.1016/j.gexplo.2024.107392.

Full text

Abstract:

In exploration geochemistry, mineral deposits are typically characterised by an enrichment of the targeted elements, and thus their element composition differs from that of samples in a local neighbourhood. Local outlier detection methods aim at identifying local changes. In contrast to conventional outlier detection procedures, local outlier detection methods are multivariate methods for outlier identification that incorporate the spatial neighbourhood of the samples. It is essential that geochemical data are treated as compositional data, and the requirements for their treatment depend on th

APA, Harvard, Vancouver, ISO, and other styles

30

Schubert, Erich, Arthur Zimek, and Hans-Peter Kriegel. "Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection." Data Mining and Knowledge Discovery 28, no. 1 (2012): 190–237. http://dx.doi.org/10.1007/s10618-012-0300-z.

Full text

APA, Harvard, Vancouver, ISO, and other styles

31

Zheng, Xiaoyu, Dexin Yu, Chen Xie, and Zhuorui Wang. "Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization." Mathematics 11, no. 3 (2023): 620. http://dx.doi.org/10.3390/math11030620.

Full text

Abstract:

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects o

APA, Harvard, Vancouver, ISO, and other styles

32

Pereira, Francisco Melo, and Rute C. Sofia. "An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories." Future Internet 15, no. 1 (2022): 4. http://dx.doi.org/10.3390/fi15010004.

Full text

Abstract:

This paper provides an analysis of two machine learning algorithms, density-based spatial clustering of applications with noise (DBSCAN) and the local outlier factor (LOF), applied in the detection of outliers in the context of a continuous framework for the detection of points of interest (PoI). This framework has as input mobile trajectories of users that are continuously fed to the framework in close to real time. Such frameworks are today still in their infancy and highly required in large-scale sensing deployments, e.g., Smart City planning deployments, where individual anonymous trajecto

APA, Harvard, Vancouver, ISO, and other styles

33

Singh, Alok Kumar. "Multivariate Analysis of Crime Data using Spatial Outlier Detection Algorithm." Journal of Statistics Applications & Probability 5, no. 3 (2016): 433–38. http://dx.doi.org/10.18576/jsap/050307.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Hosseinalizadeh, Mohsen, Firoozeh Rivaz, and Roya Hedayatizadeh. "Importance of Outlier Detection in Spatial Analysis of Wind Erosion." Procedia Environmental Sciences 7 (2011): 341–46. http://dx.doi.org/10.1016/j.proenv.2011.07.059.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Cai, Qiao, Haibo He, and Hong Man. "Spatial outlier detection based on iterative self-organizing learning model." Neurocomputing 117 (October 2013): 161–72. http://dx.doi.org/10.1016/j.neucom.2013.02.007.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Ernst, Marie, and Gentiane Haesbroeck. "Comparison of local outlier detection techniques in spatial multivariate data." Data Mining and Knowledge Discovery 31, no. 2 (2016): 371–99. http://dx.doi.org/10.1007/s10618-016-0471-0.

Full text

APA, Harvard, Vancouver, ISO, and other styles

37

Traun, Christoph, Manuela Larissa Schreyer, and Gudrun Wallentin. "Empirical Insights from a Study on Outlier Preserving Value Generalization in Animated Choropleth Maps." ISPRS International Journal of Geo-Information 10, no. 4 (2021): 208. http://dx.doi.org/10.3390/ijgi10040208.

Full text

Abstract:

Time series animation of choropleth maps easily exceeds our perceptual limits. In this empirical research, we investigate the effect of local outlier preserving value generalization of animated choropleth maps on the ability to detect general trends and local deviations thereof. Comparing generalization in space, in time, and in a combination of both dimensions, value smoothing based on a first order spatial neighborhood facilitated the detection of local outliers best, followed by the spatiotemporal and temporal generalization variants. We did not find any evidence that value generalization h

APA, Harvard, Vancouver, ISO, and other styles

38

Singh, Alok Kumar, Abhinav Singh, and Rohit Patawa. "Multiple Upper Outlier Detection Procedure in Generalized Exponential Sample." European Journal of Statistics 1, no. 1 (2021): 58–73. http://dx.doi.org/10.28924/ada/stat.1.58.

Full text

Abstract:

Hawkins [6] defined an outlier as an observation that is significantly different from the remaining observations in a dataset so as to arouse suspicion that it was generated by different mechanism. Barnett and Lewis [2] defined an outlier as an observation that deviates significantly in the sample in which it occurs. Spatial outliers are different from outliers and many authors like Singh and Lalitha [9]. Outlier detection procedures for two parameter gamma distribution have been discussed by many authors. But one major disadvantage of the gamma distribution is that the distribution (or surviv

APA, Harvard, Vancouver, ISO, and other styles

39

Puchhammer, Patricia, and Peter Filzmoser. "Spatially Smoothed Robust Covariance Estimation for Local Outlier Detection." Journal of Computational and Graphical Statistics 33:3 (December 21, 2023): 928–40. https://doi.org/10.1080/10618600.2023.2277875.

Full text

Abstract:

The article is published in the Journal of Computational and Graphical Statistics vol. 33, 2024. This article introduces a new outlier detection method that accounts for a (continuously) varying covariance structure, depending on the spatial neighborhood of the observations, which may be applied to design geochemical and geophysical vectors that can be applied at regional scale (green-field) exploration delineating high potential areas in EU. The article is funded by the SEMACRET (Sustainable exploration of critical raw materials) project.  SEMACRET aims to promote sustainable exploration

APA, Harvard, Vancouver, ISO, and other styles

40

朱, 跃忠. "Spatial Outlier Detection Based on TEN Mod-el and Weighted Attribute." Computer Science and Application 09, no. 01 (2019): 1–8. http://dx.doi.org/10.12677/csa.2019.91001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Pu, Juhua, Yue Wang, Xinran Liu, and Xiangliang Zhang. "STLP-OD: Spatial and Temporal Label Propagation for Traffic Outlier Detection." IEEE Access 7 (2019): 63036–44. http://dx.doi.org/10.1109/access.2019.2916853.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Yujun, Chen, Pu Juhua, Du Jiahong, Wang Yue, and Xiong Zhang. "Spatial–temporal traffic outlier detection by coupling road level of service." IET Intelligent Transport Systems 13, no. 6 (2019): 1016–22. http://dx.doi.org/10.1049/iet-its.2018.5214.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Fadlilah, Estannisa Asfarina. "Identifikasi Anomali Data Akademik Menggunakan Dbscan Outlier Detection." Prosiding Sains Nasional dan Teknologi 12, no. 1 (2022): 336. http://dx.doi.org/10.36499/psnst.v12i1.7012.

Full text

Abstract:

DBSCAN (Density Based Spatial Klastering of Aplikasi dengan Noise) adalah salah satu algoritma pengelompokan berbasis kepadatan. Pada penelitian ini memakai data akademik. Dengan mencari kelas atau klaster ideal pada metode DBSCAN terdapat banyak cara dalam menentukan hal tersebut. Salah satunya dengan metode Elbow. Hasil dari ini akan dijadikan dasar penentuan jumlah klaster dalam melakukan proses clustering dengan metode DBSCAN. Adanya outlier pada dataset sering dianggap sebagai salah perhitungan, oulier dapat membawa informasi yang signifikan atau informasi penting tidak ada pada data peng

APA, Harvard, Vancouver, ISO, and other styles

44

Ma, Peipei, and Guosheng Li. "Comparison and Analysis of Detection Methods for Typhoon-Storm Surges Based on Tide-Gauge Data—Taking Coasts of China as Examples." International Journal of Environmental Research and Public Health 20, no. 4 (2023): 3253. http://dx.doi.org/10.3390/ijerph20043253.

Full text

Abstract:

Global warming is predicted to lead to a new geographic and spatial distribution of storm-surge events and an increase in their activity intensity. Therefore, it is necessary to detect storm-surge events in order to reveal temporal and spatial variations in their activity intensity. This study attempted to detect storm-surge events from the perspective of detecting outliers. Four common outlier-detection methods, the Pauta criterion (PC), Chauvenet criterion (CC), Pareto distribution (PD) and kurtosis coefficient (KC), were used to detect the storm-surge events from the hourly residual water l

APA, Harvard, Vancouver, ISO, and other styles

45

Xiaowu Zhang, Siyu Jin, Xinyi Liu, Tian Tian, Zesan Liu, Tiansheng Gao,. "A Long-and Short-term Hot User Identification Method Based on Local Outlier Density." Journal of Electrical Systems 20, no. 2 (2024): 1022–29. http://dx.doi.org/10.52783/jes.1274.

Full text

Abstract:

Power systems have many users; however, there are relatively few users focused on high-quality power supplies (namely, hot users), such as external query users, electricity theft, and information fraud from billions of users. Starting from power load data, this paper postulates that abnormal electricity load users are essential components of hot users. According to the characteristics of regional power loads, this paper first builds a cross-judgment method for abnormal loads to improve the identification accuracy, including spatial and temporal abnormal load detection methods. As the spatial m

APA, Harvard, Vancouver, ISO, and other styles

46

Hosseini, Kourosh, Leonhard Reindl, Lukas Raffl, Wolfgang Wiedemann, and Christoph Holst. "3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners." Remote Sensing 16, no. 1 (2023): 138. http://dx.doi.org/10.3390/rs16010138.

Full text

Abstract:

Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to

APA, Harvard, Vancouver, ISO, and other styles

47

Michel, Bobbia, Misiti Michel, Misiti Yves, Poggi Jean-Michel, and Portier Bruno. "Spatial outlier detection in the PM 10 monitoring network of Normandy (France)." Atmospheric Pollution Research 6, no. 3 (2015): 476–83. http://dx.doi.org/10.5094/apr.2015.053.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Hutchison, Taylor A., Brian D. Welch, Jane R. Rigby, et al. "TEMPLATES: A Robust Outlier Rejection Method for JWST/NIRSpec Integral Field Spectroscopy." Publications of the Astronomical Society of the Pacific 136, no. 4 (2024): 044503. http://dx.doi.org/10.1088/1538-3873/ad34fd.

Full text

Abstract:

Abstract We describe a custom outlier rejection algorithm for JWST/NIRSpec integral field spectroscopy. This method uses a layered sigma clipping approach that adapts clipping thresholds based upon the spatial profile of the science target. We find that this algorithm produces a robust outlier rejection while simultaneously preserving the signal of the science target. Originally developed as a response to unsatisfactory initial performance of the jwst pipeline outlier detection step, this method works either as a standalone solution, or as a supplement to the current pipeline software. Compari

APA, Harvard, Vancouver, ISO, and other styles

49

Carrilho, A. C., M. Galo, and R. C. Santos. "STATISTICAL OUTLIER DETECTION METHOD FOR AIRBORNE LIDAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1 (September 26, 2018): 87–92. http://dx.doi.org/10.5194/isprs-archives-xlii-1-87-2018.

Full text

Abstract:

<p><strong>Abstract.</strong> Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external inf

APA, Harvard, Vancouver, ISO, and other styles

50

Elashry, A., B. Sluis, and C. Toth. "IMPROVING RANSAC FEATURE MATCHING BASED ON GEOMETRIC RELATION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 321–27. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-321-2021.

Full text

Abstract:

Abstract. Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!