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IEEE Radar Conference

作者： Yang, Yue Gui, Guan Zhang, Xuejing Long, Keyu Zha, Zhenzhu Wan, Qun Univ Elect Sci & Technol China Dept Elect Engn Chengdu Sichuan Peoples R China Nanjing Univ Posts & Telecommun Nanjing Jiangsu Peoples R China Second Res Inst CAAC Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538641675

In this paper, we propose an efficient joint imaging and coherent change detection (CCD) algorithm to cope with the issue of interrupted synthetic aperture radar (SAR) environments. Information about the changing scenes is considered using a partially coherent model, where the structure characteristics of changes are modeled by a Markov random fields (MRF) prior. Then the variational Bayesian expectation-maximization (VBEM) algorithm is employed to simultaneously approximate the posterior distributions of the change map and scene estimates. The proposed scheme has a superior change detection performance over the classical coherent change detectoris. Representative simulations are conducted to demonstrate the validity of the devised algorithm.

关键词： Charge coupled devices change detection algorithms Bayes methods Azimuth Matching pursuit algorithms Synthetic aperture radar Imaging

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SUPERPIXEL-BASED change detection IN HIGH RESOLUTION SAR IMAGES USING REGION COVARIANCE FEATURES 8

SUPERPIXEL-BASED CHANGE DETECTION IN HIGH RESOLUTION SAR IMA...

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2015 8th International Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp)

作者： Huang, Xiaojing Yang, Wen Xia, Gui-Song Liao, Mingsheng Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Wuhan Univ State Key Lab LIESMARS Wuhan 430079 Peoples R China

ISBN: (纸本)9781467371193

Feature representation is very important for high resolution synthetic aperture radar (SAR) image interpretation, especially for unsupervised change detection. In this paper we propose a superpixel-based change detection approach that utilize region covariance as feature representation. After segmenting SAR images into superpixels, the second order statistic of SAR feature vectors, i.e., the region covariance feature is extracted for each superpixel. In the difference map generation stage, the dissimilarities of corresponding superpixel pairs in multi-temporal SAR images are measured by calculating the Bartlett distances between region covariance features. After that, an adaptive thresholding method is applied to obtain the final detection results. Two multi-temporal TerraSAR-X high resolution SAR image sets are tested for the proposed approach and promising results are achieved.

关键词： change detection algorithms Covariance matrices Feature extraction Histograms Image resolution Image segmentation Synthetic aperture radar

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change detection in SAR Images Using Multiobjective Optimization and Ensemble Strategy

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2021年第9期18卷 1585-1589页

作者： Liu, Ruochen Wang, Ruinan Huang, Junjun Li, Jianxia Jiao, Licheng Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China

This letter puts forward a new algorithm, ensemble strategy multiobjective fuzzy clustering method (ESMOFCM). To fully combine the gray information and spatial information of neighbor pixels, a new dividing fluctuant parameter is proposed for producing a difference image. Then, we use a frame based on multiobjective fuzzy clustering to alleviate the contradiction between removing noise and preserving details in images. Ensemble strategy is adopted to integrate all Pareto optimal solutions. The experimental results show that the proposed algorithm is superior to comparison algorithms.

关键词： change detection algorithms Microsoft Windows Clustering algorithms Radar polarimetry Pareto optimization Speckle Clustering ensemble strategy evolutionary multiobjective image change detection

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Differential Evolution Algorithm with Learning Selection Strategy for SAR Image change detection

Differential Evolution Algorithm with Learning Selection Str...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Chen, Feng Shi, Jiao Ma, Yunhong Lei, Yu Gong, Maoguo Northwestern Polytech Univ Sch Elect & Informat 127 West Youyi Rd Xian 710072 Shaanxi Peoples R China Xidian Univ Minist Educ China Key Lab Intelligent Percept & Image Understanding 2 South Taibai Rd Xian 710071 Shaanxi Peoples R China

ISBN: (纸本)9781509046010

Image change detection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes. To tackle the problem, a novel differential evolution algorithm with an automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly pre-classifies all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the non-selected population with a low probability into selected ones. Finally, the samples including partial individuals from the selected and non-selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategies in every generation, which will significantly accelerate the convergence speed. The proposed change detection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.

关键词： Algorithm design and analysis change detection algorithms Sociology Statistics Clustering algorithms Neural networks Next generation networking

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Signed Distance Based Reconstruction for Exploration and change detection in Underground Mining Disaster Prevention

Signed Distance Based Reconstruction for Exploration and Cha...

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IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

作者： Koch, Philipp May, Stefan Engelhardt, Helmut Ziegler, Jasmin Nuechter, Andreas Inst Technol Georg Simon Ohm Nurnberg Germany Informat VII Robot & Telemat Wurzburg Germany

ISBN: (纸本)9781728107783

This publication describes an application of a Truncated Signed Distance Mapping approach for disaster intervention in underground mine shafts through geometrical change detection of the shaft walls. The paper describes two main problems of such an approach (aligning two potentially huge point clouds and automatic change detection by comparing the reconstructed volumes) and explains in detail the proposed solution.

关键词： Three-dimensional displays Shafts Image reconstruction Sensors Prototypes Cameras change detection algorithms

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change detection with the Kernel Cumulative Sum Algorithm 58

Change Detection with the Kernel Cumulative Sum Algorithm

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58th IEEE Conference on Decision and Control (CDC)

作者： Flynn, Thomas Yoo, Shinjae Brookhaven Natl Lab Computat Sci Initiat Upton NY 11973 USA

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113982

Online change detection involves monitoring a stream of data for changes in the statistical properties of incoming observations. A good change detector will detect any changes shortly after they occur, while raising few false alarms. Although there are algorithms with confirmed optimality properties for this task, they rely on the exact specifications of the relevant probability distributions and this limits their practicality. In this work we describe a kernel-based variant of the Cumulative Sum (CUSUM) change detection algorithm that can detect changes under less restrictive assumptions. Instead of using the likelihood ratio, which is a parametric quantity, the Kernel CUSUM (KCUSUM) algorithm compares incoming data with samples from a reference distribution using a statistic based on the Maximum Mean Discrepancy (MMD) non-parametric testing framework. The KCUSUM algorithm is applicable in settings where there is a large amount of background data available and it is desirable to detect a change away from this background setting. Exploiting the random-walk structure of the test statistic, we derive bounds on the performance of the algorithm, including the expected delay and the average time to false alarm.

关键词： Kernel Delays change detection algorithms Testing Zinc Detectors Probability distribution

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A Computationally Efficient Algorithm for Quickest change detection in Anonymous Heterogeneous Sensor Networks

A Computationally Efficient Algorithm for Quickest Change De...

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IEEE International Symposium on Information Theory (ISIT)

作者： Sun, Zhongchang Li, Qunwei Zhang, Ruizhi Zou, Shaofeng Univ Buffalo State Univ New York Buffalo NY 14260 USA Ant Financial Hangzhou Peoples R China Univ Nebraska Lincoln NE USA

ISBN: (纸本)9781538682098

The problem of quickest change detection in anonymous heterogeneous sensor networks is studied. The sensors are clustered into K groups, and different groups follow different data generating distributions. At some unknown time, an event occurs in the network and changes the data generating distribution of the sensors. The goal is to detect the change as quickly as possible, subject to false alarm constraints. The anonymous setting is studied, where at each time step, the fusion center receives unordered samples without knowing which sensor each sample comes from, and thus does not know its exact distribution. In In an optimal algorithm was provided, which however is not computational efficient for large networks. In this paper, a computationally efficient test is proposed and a novel theoretical characterization of its false alarm rate is further developed.

关键词： change detection algorithms Approximation algorithms Computational efficiency Information theory

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change detection for finite dimensional Gaussian linear systems - a bound for the almost sure false alarm rate

Change detection for finite dimensional Gaussian linear syst...

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European Control Conference (ECC)

作者： Gerencser, Laszlo Prosdocimi, Cecilia Vago, Zsuzsanna Hungarian Acad Sci MTA SZTAKI Budapest Hungary LUISS Univ Rome Italy Catholic Univ Budapest Hungary

ISBN: (纸本)9783033039629

We consider the problem of change detection in the context of finite dimensional Gaussian linear systems. In particular a known initial system will be tested for eventual changes against a known alternative using a simplified version of the Page-Hinkley or CUSUM detector. We show that the detector is L-mixing, implying the existence of an almost sure false alarm rate. The derivation of an explicit upper bound for the latter will be outlined.

关键词： false alarm probability Linear system DIMENSIONAL Gauss detector Image change detection change detection algorithms Finite

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Coordinated change detection for UAV Formations

Coordinated Change Detection for UAV Formations

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Australian Control Conference (AuCC)

作者： Bruggemann, Troy S. Ford, Jason J. Queensland Univ Technol Sch Elect Engn & Comp Sci 2 George St Brisbane Qld 4000 Australia

ISBN: (纸本)9781922107909

New tools are required to support and increase the reliability and safety of unmanned aerial vehicle (UAV) formation operations. This paper poses a new formation coordination change detection problem as a quickest detection of change in signal coordination on the basis of a worst case average detection delay cost, inspired by Lorden's criteria. This paper also poses a pragmatic nested change detection algorithm for detecting formation coordination. The proposed algorithms are evaluated on both simulated and real measurement data.

关键词： Delays Australia Unmanned aerial vehicles change detection algorithms Monitoring Random variables Reliability Unmanned Aerial Vehicle Random variable Australia vehicle measurement data delay change detection algorithms drone honey bees

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Cosine Focal Loss-based change detection for Video Surveillance Systems 16

Cosine Focal Loss-based Change Detection for Video Surveilla...

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16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

作者： Jung, Jae-Il Jang, Junik Hong, Jonghee Samsung Elect Samsung Res Seoul South Korea

ISBN: (纸本)9781728109909

change detection is one of the important tasks for video surveillance systems. A variety of learning-based approaches have been proposed, but class imbalance in training data degrades their learning efficiency. In this paper, we propose a cross entropy loss with a modulating term in cosine form to handle this class imbalance. Although the original focal loss focuses only on reducing weights for well-classified data, the proposed function is designed to preserve sufficient gradients for rare hard samples as well. This property allows a network to learn mainly from a few significant samples on which the network should focus. We validate the proposed loss through various experiments on CDNet2014 dataset, and the results show that the network trained with the proposed loss achieves better performance than other state-of-the-arts in various complex scenarios.

关键词： Training Training data Entropy change detection algorithms Deep learning Feature extraction Video surveillance

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