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检索条件"主题词=change detection algorithms"

共 1587 条记录，以下是261-270 订阅

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Quickest change detection Using Mismatched CUSUM Extended Abstract

Quickest Change Detection Using Mismatched CUSUM Extended Ab...

引用

Annual Allerton Conference on Communication, Control, and Computing

作者： Austin Cooper Sean Meyn The Department of Electrical and Computer Engineering University of Florida Gainesville FL USA

The field of quickest change detection (QCD) concerns design and analysis of algorithms to estimate the time at which an important event takes place and identify properties of the post-change behavior.

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

关键词： Power transmission lines change detection algorithms Computer bugs Testing

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Abrupt scene change detection based on the temporal activity distributions

Abrupt scene change detection based on the temporal activity...

引用

IEEE Asia-Pacific Conference on Circuits and Systems

作者： Jae Ho Chung Department of Electronic Engineering Inha University Incheon South Korea

ISBN: (纸本)0780337026

In this paper, an abrupt scene change detection algorithm is suggested. The algorithm is developed based on the temporal activity distribution that derived from the temporal subband analysis. Computer simulation results show that the proposed scheme is capable of detecting abrupt scene changes in video sequence accurately and efficiently.

关键词： Layout Frequency detection algorithms Image analysis Image sequence analysis Video sequences Performance analysis Signal analysis change detection algorithms Algorithm design and analysis

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NORMALIZED COMPRESSION DISTANCE FOR SAR IMAGE change detection

NORMALIZED COMPRESSION DISTANCE FOR SAR IMAGE CHANGE DETECTI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Mihai Coea Andrei Anghel Mihai Datcu Military Technical Academy Bucharest Romania University POLITEHNICA of Bucharest Bucharest Romania

With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types of terrain changes has greatly expanded. In this paper, a SAR image change detection method based on normalized compression distance (NCD) is proposed. The procedure mainly consists in dividing two time series images in patches, computing a collection of similarities corresponding to each pair of patches and generating the change map with a histogram-based threshold. The experimental results were computed using 2 Sentinel 1A images over the city of Bucharest, Romania and 2 TerraSAR-X images over the Elbe River and its surrounding area, Germany.

关键词： Image coding Synthetic aperture radar Satellite broadcasting change detection algorithms Time series analysis Aerodynamics Radiometry

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Two-Layer Slow Feature Analysis Network for change detection

Two-Layer Slow Feature Analysis Network for Change Detection

引用

Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Min Yang Meiling Zhang Yanfeng Gu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9781728152950

Multi-temporal remote sensing image change detection is one of the important contents of remote sensing image processing, and has important applications in many fields. Existing multi-temporal change detection mainly deals with bi-temporal images and extracts change information by ratio or difference method. This processing cannot effectively mine the change information between multiple temporal images or time series of remote sensing images. In this paper, a two-layer slow feature analysis network (SFANet) is proposed to realize effective change detection with multiple temporal remote sensing images. The proposed method extends the existing slow feature analysis method to a two-layer network structure and forms a slow feature analysis network. On the basis of multi-temporal feature extraction in slow feature analysis network, change detection is realized by threshold method. In this paper, multi-temporal high-resolution remote sensing images are used for experiments. The experimental results demonstrate that the proposed SFANet for change detection is effective and better than several commonly used methods.

关键词： Feature extraction Remote sensing Time series analysis Trajectory change detection algorithms Eigenvalues and eigenfunctions Principal component analysis

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A Bayesian Nonparametric Model for Unsupervised change detection of Fully Polarimetric SAR Images

A Bayesian Nonparametric Model for Unsupervised Change Detec...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wassim Bdiri Nizar Bouhlel Stéphane Méric Eric Pottier Fathi Kallel IETR UMR CNRS 6164 Univ Rennes Rennes cedex France Sfax Technopark Enetcom Univ Sfax Sfax Tunisia INRAE IRHS SFR QuaSaV Institut Agro Univ Angers Angers France IETR UMR CNRS 6164 INSA Rennes Rennes France

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper proposes a new method for automatic change detection in multilook polarimetric synthetic aperture radar (PolSAR) images based on unsupervised classification of these data. changes are identified by comparing multiple classification images. A Bayesian Nonparametric (BNP) approach such as the Dirichlet process mixture model (DPMM) is a potential method for classification tasks. It provides a framework for estimating both the number of components in a mixture model and the parameters of the individual mixture components simultaneously from PolSAR data. Usually, DPMM is treated using Markov chain Monte Carlo (MCMC) or variational Bayes (VB) methods. Here, we propose an expectation-maximization (EM) algorithm to estimate the parameters of the DPMM for high resolution PolSAR images which are modeled by the product model. The performance of the proposed method is evaluated with PolSAR images and the preliminary results on classification and finally on change detection are satisfactory and meet our expectations.

关键词： Image resolution Monte Carlo methods change detection algorithms Urban areas Mixture models Polarimetric synthetic aperture radar Radar polarimetry

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Unsupervised change detection using DRE-CUSUM

Unsupervised Change Detection using DRE-CUSUM

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Asilomar Conference on Signals, Systems & Computers

作者： Sudarshan Adiga Ravi Tandon Department of Electrical and Computer Engineering University of Arizona Tucson AZ USA

This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and post-change distributions are available. The core idea behind the proposed approach is to split the time-series at an arbitrary point and estimate the ratio of densities of distribution (using a parametric model such as a neural network) before and after the split point. The DRE-CUSUM change detection statistic is then derived from the cumulative sum (CUSUM) of the logarithm of the estimated density ratio. We present a theoretical justification as well as accuracy guarantees which show that the proposed statistic can reliably detect statistical changes, irrespective of the split point. While there have been prior works on using density ratio based methods for change detection, to the best of our knowledge, this is the first unsupervised change detection approach with a theoretical justification and accuracy guarantees. The simplicity of the proposed framework makes it readily applicable in various practical settings (including high-dimensional time-series data); we also discuss generalizations for online change detection. We experimentally show the superiority of DRE-CUSUM using both synthetic and real-world datasets over existing state-of-the-art unsupervised algorithms (such as Bayesian online change detection, its variants as well as several other heuristic methods). Full version of the paper available in [1].

关键词： Computers change detection algorithms Heuristic algorithms Computer network reliability Neural networks Estimation Reliability theory

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An automated change-detection algorithm for HTML documents based on semantic hierarchies

An automated change-detection algorithm for HTML documents b...

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International Conference on Data Engineering

作者： Seung-Jin Lim Yiu-Kai Ng Computer Science Department Brigham Young University Provo UT USA

ISBN: (纸本)0769510019

The data at many Web sites is changing rapidly, and a significant amount of this data is presented in HTML documents that consist of markups and data contents. Although XML is becoming more popular for data exchange, the presentation of data contained in XML documents is given, by and large, in the HTML format using XSL(T). Since HTML was designed to "display" data from the human perspective, it is not trivial for a machine to detect (hierarchical) changes of data in an HTML document. In this paper, we propose a heuristic algorithm, called SCD (Semantic change detection), to detect semantic changes to the hierarchical data contents in any two HTML documents automatically. Semantic changes differ from syntactic changes since the latter refer to changes of data contents with respect to markup structures according to the HTML grammar. SCD does not require pre-processing, nor any knowledge of the internal structure of the source documents beforehand. The time complexity of SCD is O[(|X|/spl times/|Y|)log(|X|/spl times/|Y|)], where |X| and |Y| are the number of unique branches in the syntactic hierarchies of any two given HTML documents, respectively.

关键词： change detection algorithms HTML XML Eyes Computer science Humans Heuristic algorithms Displays Testing

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Flood monitoring and change detection based on unsupervised image segmentation and fusion in multitemporal SAR imagery

Flood monitoring and change detection based on unsupervised ...

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International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)

作者： J. Avendano S.F. Mora J.E. Vera J.A. Torres F.A. Prieto Grupo de Investigacion y Desarrollo TECnologico Aplicado (INDETECA) Universidad Escuela Colombiana de Carreras Industriales Bogota Colombia Grupo de Investigacion y Desarrollo TECnologico Aplicado (INDETECA) Universidad Escuela Colombiana de Carreras Industriales Bogota Colombia Grupo de Automatica (GAUNAL) Universidad Nacional de Colombia Bogota Colombia

This paper presents an unsupervised method for change detection and analysis of the behavior of floods using multitemporal SAR (Synthetic Aperture Radar) images. First, images were filtered using the Enhanced Frost Filter in order to reduce the effect of speckle noise. Fuzzy Clustering Means (FCM) and k-means algorithms were used for unsupervised segmentation, and both results were fused using PCA. Subsequently, a Boolean image was created from the change information using a thresholding algorithm. Finally, the area of changes in the scene was calculated with spatial resolution information from the images. For the experiment phase, synthetic images were first created with varying levels of speckle noise, making it possible to evaluate the performance of the proposed method. The results showed an overall accuracy of approximately 99% and a kappa index of 0.76 for images whose Equivalent Number of Looks (ENL) equals 0.7. This shows that the proposed method is efficient in detecting changes in SAR images with an ENL greater than or equal to 0.7. Finally, two SAR images were tested, one before and one after a flood that covered an area of the Magdalena River in Colombia called Plato-Magdalena. Our method found that the river flooded approximately 131.51 hectares of terrain in the case of the studied images.

关键词： Image segmentation Speckle change detection algorithms Synthetic aperture radar Histograms Principal component analysis Fuses

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change detection in multiple-temporal Synthetic Aperture Radar images based on averaged heterogeneous factors of neighbourhood areas

Change detection in multiple-temporal Synthetic Aperture Rad...

引用

National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS)

作者： An Hung Nguyen Phat Tien Nguyen Le Quy Don Technical University Hanoi Vietnam

ISBN: (纸本)9781665410021

change detection in multiple-temporal Synthetic Aperture Radar images has been received great interests for recent decades. The basic principle of change detection is to analyse the difference images generated from two Synthetic Aperture Radar images captured in the same geographic area at two different times. The popular operators used to create difference images are traditional subtraction, ratio, logarithm based ones and modified versions of them, which can use pixel information in the local or global areas. A challenge in detecting changes is to reduce impacts of speckle noises inherently existing in Synthetic Aperture Radar images on the accuracy of the detection. This paper proposed a novel algorithm to create the difference images based on averaging heterogeneous factors of corresponding neighbourhood areas in the two images. The resultant difference image is then filtered by the average filter to reject remaining speckle noises.

关键词： Performance evaluation Computer science change detection algorithms Computational modeling Urban areas Filtering algorithms Speckle

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Three different unsupervised methods for change detection: an application

Three different unsupervised methods for change detection: a...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： A. D'Addabbo G. Satalino G. Pasquariello P. Blonda Consiglio Nazionale delle Ricerche C.N.R. Istituto di Studi sui Sistemi Intelligenti per l''Automazione-C.N.R. Bari Italy

In this work, unsupervised change detection techniques, based on three different way to compare images, are presented. Two Landsat TM registered and corrected multi-spectral images, acquired on the same geographical area on 18 May 1996 and 21 May 1997, have been used. In the first comparison technique, for each pair of corresponding pixels, the spectral change vector has been computed as the squared difference in the features vectors at the two times. In the second method, the difference image has been computed using, pixel by pixel, a chi square transformation. The third technique is based on the application of a Self-Organizing Map (SOM) neural network to clusterize the two images before comparison. The three obtained difference images has been then analyzed by using a fully automatic thresholding method exploiting the expectation-maximization (EM) algorithm. The experimental results obtained for the three difference images are comparable, showing a reliable robustness of the unsupervised approach, and only few change are detected on the analyzed scene. Moreover, the experimental results have been compared with a change detection map computed by using a supervised technique, obtaining a good agreement between unsupervised and supervised results that confirms the reliability of the considered approach. The encouraging obtained results allow to use the so-computed percentage value of changes as probability of class transitions in input to a Bayesian supervised change detection method, as presented in a companion paper by the same authors. In this framework, the unsupervised approach may be used to support supervised techniques, providing land cover transitions that can be used as guess values

关键词： Pixel Image analysis Satellites Remote sensing Multispectral imaging Neural networks Algorithm design and analysis change detection algorithms Clustering algorithms Robustness

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