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检索条件"主题词=Change detection algorithms"

共 1584 条记录，以下是351-360 订阅

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Toward Uncertainty Aware Quickest change detection

Toward Uncertainty Aware Quickest Change Detection

引用

International Conference on Information Fusion

作者： James Zachary Hare Lance Kaplan Venugopal V. Veeravalli CCDC Army Research Laboratory Adelphi MD USA University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (纸本)9781665414272

We study the problem of Quickest change detection (QCD) where the parameters of both the pre- and post-change distributions are completely unknown or known within a second-order distribution generated from training data. We propose the use of the Uncertain Likelihood Ratio (ULR) test statistic, which is designed from a Bayesian perspective in contrast with the traditional frequentist approach, i.e., the Generalized Likelihood Ratio (GLR) test. The ULR test utilizes a ratio of posterior predictive distributions, which incorporates parameter uncertainty into the likelihood estimates when there is a lack of or limited availability of training samples. Through an empirical study, we show that the proposed test outperforms the GLR test, while achieving similar results as the classical CUSUM algorithm as the number of training samples goes to infinity.

关键词： Training Uncertain systems Uncertainty change detection algorithms Conferences Training data Prediction algorithms

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Adaptive change detection

Adaptive Change Detection

引用

International Conference on Systems, Signals and Image Processing, IWSSIP

作者： A. Buades J. L. Lisani L. Rudin Universitat de les Illes Balears Palma de Mallorca Spain Cogency Technology Inc. Pasadena USA

In this paper we propose a method to detect meaningful changes between two images. By meaningful changes we refer to those due to variations in the visual content of the images, rejecting illumination changes as non-meaningful. The method has been devised in order to assure a low number of wrong detections and it depends on a few parameters. These parameters can be tuned once and for all for any input images, which makes the method virtually automatic.

关键词： Error analysis Lighting Pixel Layout change detection algorithms Laplace equations Upper bound Object detection detection algorithms Computer vision

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Copernicus Sentinels For Tillage change detection

Copernicus Sentinels For Tillage Change Detection

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： G. Satalino D. Palmisano A. Balenzano F. Lovergine F. Mattia F. Nutini M. Boschetti G. Verza M. Rinaldi S. Ruggieri F. Ciavarella C. Manganiello V. P. Gómez D. A. Nafría García Consiglio Nazionale Delle Ricerche (CNR) Istituto per il Rilevamento Elettromagnetico Dell’Ambiente (IREA) Bari Consiglio Nazionale Delle Ricerche (CNR) Istituto per il Rilevamento Elettromagnetico Dell’Ambiente (IREA) Milano Italy Consiglio per la Ricerca in Agricoltura e l’Analisi Dell’Economia Agraria (CREA) Cerealicoltura e Colture Industriali (CREA-CI) Foggia Italy Agricoltura e Ambiente (CREA-AA) Bari Italy Instituto Tecnologico Agrario de Castilla y León (ITACyL) Spain

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

An algorithm to identify and monitor tillage practices, using Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, is presented. The technique operates on agricultural fields that are either bare or sparsely vegetated. These fields are first segmented using the Normalized Difference Vegetation Index (NDVI), obtained from S-2, or the S-1 VH/VV ratio in overcast conditions. Then, a change detection approach is applied both to S-1 cross-polarized backscatter and copolarized interferometric coherence. To decouple the impact of tillage from that of moisture change on radar measurements, a two-scale strategy is used. The premise is that whereas soil moisture is primarily influenced by precipitation events happening at the medium (1.0-10 km) scale, tillage changes occur at the local, i.e., field (~0.1 km) scale. The algorithm was assessed against a multi-year ground data set collected at three sites. It includes conventional tillage change and no-tilled events. Results achieve an overall accuracy of 81%.

关键词： Normalized difference vegetation index Precipitation change detection algorithms Radar measurements Image color analysis Soil moisture Sentinel-1

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Adaptive change detection in coherent and noncoherent SAR imagery

Adaptive change detection in coherent and noncoherent SAR im...

引用

IEEE International Conference on Radar

作者： K. Ranney M. Soumekh Army Research Laboratory Adelphi MD USA Department of Electrical Engineering Suny at Buffalo Amherst NY USA

This paper is concerned with change detection in averaged multi-look SAR imagery. Averaged multi-look SAR images are preferable to full aperture SAR reconstructions when the imaging algorithm is approximation based (e.g., polar format processing), or motion data are not accurate over a long full aperture. We study the application of a SAR change detection method, known as signal subspace processing, that is based on the principles of 2D adaptive filtering (M. Soumekh, January 1999) and we use it to recognize the addition of surface landmines to a particular area under surveillance. We identify the change detection problem as a trinary hypotheses testing problem, and identify a change signal and its normalized version to determine whether there is i) no change in the imaged scene; ii) a target has entered the imaged scene; or iii) a target has exited the imaged scene. A statistical analysis of the error signal is provided to show its properties and merits. Results are provided with a realistic X band SAR platform using averaged noncoherent multi-look and coherent single-look SAR imagery.

关键词： Layout Apertures Signal processing Image reconstruction Approximation algorithms change detection algorithms Adaptive signal detection Adaptive filters Landmine detection Surveillance

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Unsupervised change detection using multitemporal spaceborne SAR data: A case study in Beijing

Unsupervised change detection using multitemporal spaceborne...

引用

Urban Remote Sensing Joint Event

作者： Yifang Ban Osama A. Yousif Department of Urban Planning & Environment Royal Institute of Technology Stockholm Sweden

The objective of this research is to examine unsupervised change detection methods using multitemporal spaceborne SAR data for urbanization monitoring in Beijing. One scene of ENVISAT ASAR C-VV image was acquired in July, 2008 and one scene of ERS-2 SAR C-VV image was acquired in July, 1998. To compare the two SAR images, a modified ratio operator that takes into account both positive and negative changes was developed to derive a change image. A generalized version of Kittler-Illingworth minimum-error thresholding algorithm was then tested to automatically classify the change image into two classes, change and no-change. Various probability density functions such as Log normal, Generalized Gaussian, Nakagami ratio, and Weibull ratio were investigated to model the distribution of the change and no-change classes. The preliminary results showed that Kittler-Illingworth algorithm applied to the modified ratio image is very effective in detecting temporal changes in urban areas using SAR images. Log normal and Nakagami density models achieved the best results. The Kappa coefficients of the these solutions were of 0.82 while the false alarm rates were 2.7%. The initial findings indicated that the accuracy of the change result obtained using Kittler-Illingworth algorithm varies depending on how the assumed conditional class density function fits the histograms of change and no-change classes.

关键词： Accuracy change detection algorithms Histograms Nakagami distribution Pixel Classification algorithms Remote sensing

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change detection IN OPTICAL REMOTE SENSING IMAGES WITH A FULLY OBJECT-LEVEL APPROACH

CHANGE DETECTION IN OPTICAL REMOTE SENSING IMAGES WITH A FUL...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Long Ma Zhihong Mai He Chen Wenchao Liu Fan Feng Guichi Liu N. Q. Soomro School of Information Engineering Zhengzhou University Zhengzhou China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Department of Software Engineering Khairpur Sindh Pakistan

In this paper, an efficient and accurate multilevel change detection method is presented, achieved by the combination of an unsupervised object-based correlation analysis and a supervised post-classification comparison. Before the change detection procedure, fast multitemporal segmentation is applied to provide object-level information on two registered images. Then the proposed object-based correlation analysis method is used to extract the potential changed areas efficiently and stably, which improves the accuracy of overall performance. Notably, all the procedures are highly automatic except for the necessary selection of training examples within all supervised algorithms. The experimental results demonstrate the superior performance of our method compared with the four typical state-of-the-art change detection methods.

关键词： Image segmentation Correlation Support vector machines Correlation coefficient Training change detection algorithms Optical imaging

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Better Together: Online Probabilistic Clique change detection in 3D Landmark-Based Maps

Better Together: Online Probabilistic Clique Change Detectio...

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2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Samuel Bateman Kyle Harlow Christoffer Heckman University of Colorado Boulder CO USA

ISBN: (数字)9781728162126

ISBN: (纸本)9781728162133

Many modern simultaneous localization and mapping (SLAM) techniques rely on sparse landmark-based maps due to their real-time performance. However, these techniques frequently assert that these landmarks are fixed in position over time, known as the static-world assumption. This is rarely, if ever, the case in most real-world environments. Even worse, over long deployments, robots are bound to observe traditionally static landmarks change, for example when an autonomous vehicle encounters a construction zone. This work addresses this challenge, accounting for changes in complex three-dimensional environments with the creation of a probabilistic filter that operates on the features that give rise to landmarks. To accomplish this, landmarks are clustered into cliques and a filter is developed to estimate their persistence jointly among observations of the landmarks in a clique. This filter uses estimated spatial-temporal priors of geometric objects, allowing for dynamic and semi-static objects to be removed from a formally static map. The proposed algorithm is validated in a 3D simulated environment.

关键词： Solid modeling Three-dimensional displays Simultaneous localization and mapping change detection algorithms Filtering algorithms Probabilistic logic Feature extraction

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Design of SAR image feature detector for small-scaled coherent change detection

Design of SAR image feature detector for small-scaled cohere...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Kee Woong Lee Hyun-Chul Kim Bumseung Kim Woo-Kyung Lee School of Electronics and Telecomm. Eng. Korea Aerospace University Goyang South Korea

Recently multi-band, high-resolution SAR images acquired from spaceborne payloads are produced on daily basis and easily available world-widely. Although the change detection techniques for SAR images have been long studied, demands for developing small-scaled change detection method algorithm are growing with the rise of SAR image features. Among others, the small-scaled change detection method is a topic of high interest in military applications for establishing future national security systems. SAR images are distinguished from optical images due to the complex nature of the inherent phase information in conjunction with amplitude parameters. The performance of detection of changes through SAR imaging are highly dependent upon both ACD(Amplitude change detection) and CCD(Coherence change detection). The ACD method, based on measurement of amplitude, is the most common approach. It simplifies the technical mechanism of imaging as it is sensitive to changes caused by artificial objects and buildings on land. The CCD method, using the information on phase, measures the extent of similarities of multi-temporal images or their coherences. In this paper, CCD algorithms are adopted to detect local changes in dense urban areas acquired from spaceborne SAR payloads. It will be further perceived to construct efficient monitoring systems on strategic target areas.

关键词： Synthetic aperture radar Coherence Charge coupled devices change detection algorithms Optical sensors Detectors Adaptive optics

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Data-efficient quickest change detection in distributed and multi-channel systems

Data-efficient quickest change detection in distributed and ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Taposh Banerjee Venugopal V. Veeravalli Dept of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign USA

ISBN: (纸本)9781479903573

A distributed or multi-channel system consisting of multiple sensors is considered. At each sensor a sequence of observations is taken, and at each time step, a summary of available information is sent to a central decision maker, called the fusion center. At some point of time, the distribution of observations at an unknown subset of the sensor nodes changes. The objective is to detect this change as quickly as possible, subject to constraints on the false alarm rate, the cost of observations taken at the sensors and the cost of communication between the sensors and the fusion center. Minimax formulations are proposed for this problem. An algorithm called DE-Censor-Sum is proposed, and is shown to be asymptotically optimal for the proposed formulations, for each possible post-change scenario, as the false alarm rate goes to zero. It is also shown, via numerical studies, that the DE-Censor-Sum algorithm performs significantly better than the approach of fractional sampling, where the cost constraints are met based on the outcome of a sequence of biased coin tosses, independent of the observation process.

关键词： Sensor fusion Delays change detection algorithms Approximation algorithms Sensor systems Random variables

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Current Transformer Oil Leak detection Algorithm Based on change detection and Grayscale Histogram Double Gaussian Fitting

Current Transformer Oil Leak Detection Algorithm Based on Ch...

引用

IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Mei Ma Tailong Chen Tianlong Yang Lin Guo Jun Liao State Grid Sichuan Information& Telecommunications Company Chengdu China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Current transformers are important equipment in unattended substations. Its operating conditions must be monitored in real time, and corresponding alarms should be generated when abnormal conditions occur. A current transformer oil leak detection algorithm based on change detection and grayscale histogram double Gaussian fit analysis is proposed in this paper. The ViBe algorithm is used to determine the anomalous region, and the gray histogram of the anomalous region is subjected to double Gaussian fitting analysis. The analysis results can be used to determine whether there is oil leakage in the anomalous region. Experiments show that the method introduced in this paper can detect abnormal oil leakage of current transformer more accurately , and can well eliminate the impacts of external factors such as changes in light conditions, shadows, and rain. It also provides support for monitoring and early warning of current transformers.

关键词： Histograms Substations Current transformers change detection algorithms Oils Fitting Monitoring

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