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Unsupervised change detection Using Multiscale and Multiresolution Gaussian-Mixture-Model Guided by Saliency Enhancement

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1796-1809页

作者： Xue, Dinghua Lei, Tao Jia, Xiaohong Wang, Xingwu Chen, Tao Nandi, Asoke K. Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Shaanxi Joint Lab Artificial Intelligence Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artificial Intelligence Xian 710021 Peoples R China Shaanxi Univ Sci & Technol Sch Elect Informat & Artif Intelligence Xian 710021 Peoples R China China Univ Geosci Inst Geophys & Geomat Wuhan 430074 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China

Popular unsupervised change detection algorithms suffer from two problems: first, the difference image generated by bitemporal images usually includes a large number of falsely changed regions due to noise corruption and illumination change;second, fuzzy clustering algorithms are sensitive to noise and they miss the relationship among feature components. To address these issues, we propose a multiscale and multiresolution Gaussian-mixture-model guided by saliency-enhancement (SE-MGMM) for change detection in bitemporal remote sensing images. The proposed SE-MGMM makes two contributions. The first is a novel salient strategy that can enhance saliency objects while suppressing the image background. The strategy uses the saliency weight information to enhance changed regions leading to the improvement of grayscale contrast between changed regions and unchanged regions. The second is that we present a Gaussian-mixture-model based on spatial multiscale and frequency multiresolution information fusion, which can effectively utilize features of difference images and improve detection results of changed regions. Experiments show that the proposed SE-MGMM is robust for both very high-resolution remote sensing images and synthetic aperture radar images. Moreover, the SE-MGMM achieves better change detection and provides better performance metrics than state-of-the-art approaches.

关键词： Task analysis Feature extraction Lighting Level set Gray-scale change detection algorithms Satellites change detection feature fusion Gaussian-mixture-model (GMM) saliency enhancement

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Teletext data change detection and noiseless data compression

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 1995年第4期41卷 1061-1068页

作者： Siu, YM Chan, CK Ho, KL UNIV HONG KONG DEPT ELECT & ELECTR ENGNHONG KONGHONG KONG

Full Channel teletext system is a high speed data broadcasting system. Pages of information are broadcast in a cyclic manner. The detection of data change in the information pages is necessary for data analysis, database update and retransmission. Lossless data compression is also necessary to enhance the data throughput in rebroadcasting and to reduce the storage requirement. Performing data change detection and data compression in real time using a software approach in a small machine is impossible for such high speed data. In this paper, we describe the algorithms that are suitable for hardware implementation for both data change detection and noiseless data compression.

关键词： Teletext Data compression Broadcasting Cyclic redundancy check Data analysis Databases Throughput Software performance change detection algorithms Hardware

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Real-Time change Point detection with Application to Smart Home Time Series Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第5期31卷 1010-1023页

作者： Aminikhanghahi, Samaneh Wang, Tinghui Cook, Diane J. Washington State Univ Sch Elect Engn & Comp Sci Pullman WA 99164 USA

change Point detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. In this paper, we present a novel real-time nonparametric change point detection algorithm called SEP, which uses Separation distance as a divergence measure to detect change points in high-dimensional time series. Through experiments on artificial and real-world datasets, we demonstrate the usefulness of the proposed method in comparison with existing methods.

关键词： Activity transition detection change detection algorithms separation distance smart homes time series data

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A Bayesian Approach to Sequential change detection and Isolation Problems

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第3期67卷 1796-1803页

作者： Chen, Jie Zhang, Wenyi Poor, H. Vincent Univ Sci & Technol China CAS Key Lab Wireless Opt Commun Hefei 230027 Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

The problem of sequential change detection and isolation under the Bayesian setting is investigated, where the change point is a random variable with a known distribution. A recursive algorithm is proposed, which utilizes the prior distribution of the change point. We show that the proposed decision procedure is guaranteed to control the false alarm probability and the false isolation probability separately under certain regularity conditions, and it is asymptotically optimal with respect to a Bayesian criterion.

关键词： Bayes methods Delays Upper bound change detection algorithms Random variables Stochastic systems Probability distribution Asymptotic behavior average detection delay Bayesian change detection change detection and isolation decision procedures

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OPTIMUM MULTISENSOR DATA FUSION FOR IMAGE change detection

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS 1995年第9期25卷 1340-1347页

作者： JAIN, ZS CHAU, YGA Department of Electrical Engineering Yuan Ze Institute of Technology Chungli Taiwan

Optimum multisensor data fusion is addressed for image change detection based on the optimum likelihood ratio test for the statistical dependence of the luminance signals in additive Gaussian noise. It is demonstrated that the information to be transmitted from the sensors to-the fusion center is the maximum likelihood estimates of the correlation coefficients between pairs of consecutive image frames. Experimental results illustrate that the detection error decreases as the number of sensors and/or frames increases.

关键词： Image sensors Sensor fusion Testing Maximum likelihood estimation Light rail systems Gaussian noise change detection algorithms Statistics Multisensor systems Sensor systems

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Kernel-Based Edge-Preserving Methods for Abrupt change detection

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IEEE SIGNAL PROCESSING LETTERS 2020年第0期27卷 86-90页

作者： Xiang, Shiming Tang, Bo Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

Abrupt change detection is critical to monitor the occurrence of abnormal events from sensor data for situational awareness of complex systems. However, various disturbances and noises applied to the data observations may pose significant challenges to the robustness of many abrupt change detection methods. Recent researches have shown that bilateral filter can acquire outstanding performance on removing noises from images while preserving edge information. In this letter, we propose two improved edge-preserving memory-based cumulative sum (MB-CUSUM) methods that are able to make the abrupt change detection method more robust against noises. Our experimental studies show that the proposed methods can achieve superior performance over state-of-the-art methods to detect abrupt changes, which demonstrates the effectiveness and feasibility of their practical use.

关键词： Kernel Microsoft Windows Image edge detection Euclidean distance Signal processing algorithms Probability distribution change detection algorithms Abrupt change detection edge preserving bilateral filter event detection

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change Point detection using the Kantorovich Distance Algorithm

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IFAC-PapersOnLine 2018年第18期51卷 708-713页

作者： Arifin, B.M.S. Li, Zukui Shah, Sirish L. Department of Chemical and Materials Engineering University of Alberta EdmontonT6G 2V4 Canada

In this article, a novel change detection algorithm is proposed based on the Kantorovich distance concept. Incorporating the proposed change detection algorithm with the existing process monitoring tools may assist the operator in detecting dynamic changes in process plants and provide fewer unnecessary (false) alarms as well as fewer missed alarms. The proposed change detection method was tested through simulation data. It is applied to the benchmark Tennessee Eastman (TE) process in online mode. Results prove the efficacy of the proposed method to detect both the sharp and incipient changes. © 2018

关键词： Linear programming Process monitoring Signal detection change detection change detection algorithms change point detection Distance algorithm Dynamic changes Kantorovich Distance Tennessee Eastman Transient process

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Comparison of Elevation change detection Methods From ICESat Altimetry Over the Greenland Ice Sheet

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2017年第10期55卷 5494-5505页

作者： Felikson, Denis Urban, Timothy J. Gunter, Brian C. Pie, Nadege Pritchard, Hamish D. Harpold, Robert Schutz, Bob E. Univ Texas Austin Austin TX 78712 USA Georgia Inst Technol Atlanta GA 30332 USA British Antarctic Survey Cambridge CB3 0ET England NOAA Silver Spring MD 20910 USA

Estimation of the surface elevation change of the Greenland Ice Sheet (GrIS) is essential for understanding its response to recent and future climate change. Laser measurements from the NASA's Ice, Cloud, and land Elevation Satellite (ICESat) created altimetric surveys of GrIS surface elevations over the 2003-2009 operational period of the mission. This paper compares four change detection methods using Release 634 ICESat laser altimetry data: repeat tracks (RTs), crossovers (XOs), overlapping footprints (OFPs), and triangulated irregular networks (TINs). All four methods begin with a consistently edited data set and yield estimates of volumetric loss of ice from the GrIS ranging from -193 to -269 km(3)/yr. Using a uniform approach for quantifying uncertainties, we find that volume change rates at the drainage system scale from the four methods can be reconciled within 1-sigma uncertainties in just 5 of 19 drainage systems. Ice-sheet-wide volume change estimates from the four methods cannot be reconciled within 1-sigma uncertainties. Our volume change estimates lie within the range of previously published estimates, highlighting that the choice of method plays a dominant role in the scatter of volume change estimates. We find that for much of the GrIS, the OFP and TIN methods yield the lowest volume change uncertainties because of their superior spatial distribution of elevation change rate estimates. However, the RT and XO methods offer inherent advantages, and the future work to combine the elevation change detection methods to produce better estimates is warranted.

关键词： Altimetry change detection algorithms Greenland ice thickness remote sensing

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Land Cover change detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features

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IEEE ACCESS 2020年 8卷 228070-228087页

作者： Jing, Ran Gong, Zhaoning Guan, Hongliang Yangtze Univ Sch Geosci Wuhan 430100 Peoples R China Capital Normal Univ Coll Resource Environm & Tourism Beijing 100048 Peoples R China Capital Normal Univ Coll Geospatial Informat Sci & Technol Beijing 100048 Peoples R China

change detection with very high resolution (VHR) satellite images is of great application values when evaluating and monitoring land use changes. However, intrinsic complexity of satellite images will introduce more difficulties to change detection tasks. In this study, a new change detection method is proposed by combining multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) with stacked convolutional auto-encoder (SCAE) features to improve change detection capabilities with VHR satellite images. First, the multi-scale SLIC-based image segmentation is performed on multi-temporal images to generate segment objects while keeping their edge information as much as possible. Second, the convolutional layers in a CNN architecture are used to generate change map, then, an SCAE feature-based classification procedure is performed to generate "from-to" change information. Finally, a Bayesian information criterion is used to optimize the results of change detection. In this study, the experiments carried out reveal that the multi-scale SLIC image segmentation algorithm affects the integrity of change regions;CNN features have an effect on the consistency of change regions;and SCAE features influence the performance of support vector machine (SVM) classifiers. And, features extracted from the architectures enhance the ability of information extraction from ground objects. Comparison results also show the superiority to other change detection methods.

关键词： Image segmentation Feature extraction Satellites change detection algorithms Spatial resolution Computer architecture detection algorithms change detection image segmentation multi-scale simple linear iterative clustering-convolutional neural network (SLIC-CNN) stacked convolutional auto-encoder (SCAE) VHR satellite imagery

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A Nonparametric Estimator for Coherent change detection: The Permutational change detection

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Costa, Giovanni Monti-Guarnieri, Andrea Virgilio Manzoni, Marco Rucci, Alessio Politecn Milan Dipartimento Elettron Informaz & Bioingn I-20133 Milan Italy TRE ALTAMIRA Srl I-20143 Milan Italy

Nowadays, synthetic aperture radar (SAR) is widely used in heterogeneous fields with aims strictly dependent on the objectives of the application. One of the most common is the exploitation of the interferometric-SAR (InSAR) to measure millimeter movements on the Earth's surface, aiming to monitor failures (e.g., landslides) or to measure the health state of infrastructures (e.g., mining assets, bridges, and buildings). In this article, developing algorithms to detect temporal and spatial changes in the radar targets becomes very important. This article focuses on the temporal change detection framework, proposing a nonparametric coherent change detection (CCD) algorithm called permutational change detection (PCD), a purely statistical algorithm whose core is the permutational test. The PCD estimates the temporal change points (CPs) of a radar target recognizing blocks structure in the coherence matrix, namely, new radar objects. The algorithm has been fine-tuned for small SAR datasets, with the specific aim of prioritizing the analysis of the latest changes. A rigorous mathematical derivation of the algorithm is carried out, explaining how some limits have been addressed. Then, the performance analysis on the simulated data is deeply accomplished, carried out for the stand-alone PCD and the PCD compared with a parametric CCD algorithm based on the generalized likelihood ratio test (GLRT), and with the Omnibus and REACTIV detectors. The comparison with these other algorithms and the stand-alone performance analysis point out the robustness of the PCD in dealing with very noisy environments, even in the case of a single block. Finally, the PCD is validated by processing two Sentinel I data stacks, ascending and descending geometries, of the 2016 Central Italy earthquake.

关键词： Coherence Radar imaging Charge coupled devices change detection algorithms Decorrelation Sociology Radar detection Blocks structure change points (CPs) interferometric synthetic aperture radar (InSAR) nonparametric coherent change detection (CCD) permutational tests synthetic aperture radar (SAR)

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