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AFRICON Conference

作者： D.W. Ngwenya G.P. Hancke Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

We have employed principles from change detection algorithms and statistical process control (SPC) to improve the estimation of the average of the round-trip time (RTT). Two mechanisms are proposed. The first is based on SPC principles. The other is based on an intuition derived from the log-likelihood ratio concept. Data employed to test the mechanisms are obtained by pinging several nodes on a campus network. Both methods improved the ability to track the change in the mean and take appropriate action

关键词： detection algorithms Jacobian matrices Filters change detection algorithms Process control Gaussian distribution Data engineering Testing Noise robustness History

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Research on change detection algorithms for Image Interaction and Edge Enhancement

Research on Change Detection Algorithms for Image Interactio...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Qi Wang Jiying Li School of Electronic and Information Engineering Lanzhou Jiaotong University Lan Zhou China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

Most of the existing detection methods ignore the spatial and temporal characteristics of the two images and global dependence of the interconnections, resulting in many irrelevant changes (such as illumination, the atmosphere, seasonal changes in vegetation) to produce the phenomenon of misdetection; at the same time, the existing methods of change detection. Meanwhile, the existing change detection methods are not easy to obtain structural details in high-resolution remote sensing images and the detection accuracy needs to be improved. Based on the global information interaction and edge detection method of dual-time-phase image, a network model is proposed, which consists of three parts and is divided into three modules, namely, edge structure and image feature extraction, image transformer and change region discrimination. Edge detection is performed on the dual-temporal phase image to obtain the edge map, edge detection can effectively improve the edge details of the detection results, the edge map and the original image are fused for feature extraction, and the extracted dual-temporal phase features are sent to the image transformer BIT for global spatial-temporal modeling to establish the connection, modeling the remote context in the dual-temporal image in an efficient and effective way, and finally through the simple region of change discriminative module for discrimination obtaining the detection results. The algorithm achieves 96.2% F1 on CDD dataset and 93.8% F1 on BCDD dataset, which is superior with better stability and robustness compared with some known algorithms.

关键词： change detection algorithms Image edge detection Atmospheric modeling Vegetation mapping Lighting Feature extraction Transformers

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Performance comparison of two sequential change detection algorithms on detection of in-band wormholes

Performance comparison of two sequential change detection al...

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Annual Conference on Information Sciences and Systems (CISS)

作者： Shanshan Zheng Tao Jiang John S. Baras Institute of Systems Research Department of Electrical and Computer Engineering University of Maryland College Park MD USA

This paper compares the performance of parametric and non-parametric sequential change detection algorithms for detecting in-band wormholes in wireless ad hoc networks. The algorithms considered are the non-parametric cumulative sum (NP-CUSUM) and the repeated sequential probability ratio test (R-SPRT). Theoretical performance of the two is compared using metrics that take into account the algorithms' repeated nature, and the advantage of the parametric method is illustrated. On the other hand, connections between the parametric and non-parametric methods are made in the proposed worst case adversary model, where the non-parametric method is shown to be more robust to attack strategy changes. Experimental evaluation of wormhole detection schemes based on the two algorithms is presented. This work has implications for both the theoretical understanding and practical design of wormhole detection schemes based on parametric and nonparametric change detection algorithms.

关键词： detection algorithms change detection algorithms Tunneling Mobile ad hoc networks Algorithm design and analysis Sequential analysis Timing Statistical analysis Routing Delay

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An immunological approach to change detection: algorithms, analysis and implications 96

An immunological approach to change detection: algorithms, a...

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IEEE Symposium on Security and Privacy

作者： P. D'haeseleer S. Forrest P. Helman Dept. of Comput. Sci. New Mexico Univ. Albuquerque NM USA Department of Computer Science University of New Mexico Albuquerque NM USA

ISBN: (纸本)9780818674174

We present new results on a distributable change-detection method inspired by the natural immune system. A weakness in the original algorithm was the exponential cost of generating detectors. Two detector-generating algorithms are introduced which run in linear time. The algorithms are analyzed, heuristics are given for setting parameters based on the analysis, and the presence of holes in detector space is examined. The analysis provider a basis for assessing the practicality of the algorithms in specific settings, and some of the implications are discussed.

关键词： change detection algorithms Algorithm design and analysis Detectors Protection Object detection Computer science Immune system Costs Heuristic algorithms Computer security

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Wavelet transforms and change-point detection algorithms for tracking network traffic fractality

Wavelet transforms and change-point detection algorithms for...

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Conference on Next Generation Internet Networks, NGI

作者： P. Zuraniewski D. Rincon Department of Applied Mathematics AGH University of Science and Technology Cracow Poland Department of Telematics Engineering Technical University of Catalonia Barcelona Spain

Network traffic is known to present fractal characteristics such as LRD, which can be efficiently measured with wavelet transforms. Current methods of estimation of fractal parameters, such as the LogScale diagram, are not able to track the changes of the parameters. This paper proposes and studies the combined use of wavelet transforms (DWT, MODWT, DTWT) and change-point detection algorithms (ICSS, SIC) in order to detect the instants when fractality changes noticeably. The different approaches are contrasted and statistical assessment is provided, together with the results of the procedure when applied to synthetic traces

关键词： Wavelet transforms detection algorithms Telecommunication traffic Fractals Discrete wavelet transforms change detection algorithms Silicon carbide Communication system traffic control Parameter estimation Bandwidth

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Network and Dataset for Multiscale Remote Sensing Image change detection

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 2851-2866页

作者： Liu, Shenbo Zhao, Dongxue Zhou, Yuheng Tan, Ying He, Huang Zhang, Zhao Tang, Lijun Changsha Univ Sci & Technol Sch Phys & Elect Sci Changsha 410114 Peoples R China

Remote sensing image change detection (RSCD) aims to identify differences between remote sensing images of the same location at different times. However, due to the significant variations in the size and appearance of objects in real-world scenes, existing RSCD algorithms often lack strong capabilities in extracting multiscale features, thereby failing to fully capture the characteristics of changes. To address this issue, a multiscale remote sensing change detection network (MSNet) and a multiscale RSCD dataset (MSRS-CD) are proposed. A multiscale convolution module (MSCM) is investigated, and combined with MSCM, an encoder capable of capturing features of different sizes is designed to efficiently extract multiscale semantic change features. A global multiscale feature fusion module is designed to achieve global multiscale feature fusion and obtain multiscale high-level semantic change features. As existing RSCD datasets lack rich scale information and often focus on change targets of specific sizes, a new dataset, MSRS-CD, is constructed. This dataset consists of 842 pairs of images with a resolution of 1024 x 1024 pixels, featuring uniformly distributed change detection target sizes. Comparative experiments are conducted with 10 other state-of-the-art algorithms on the MSRS-CD dataset and another public dataset, LEVIR-CD. Experimental results demonstrate that MSNet achieves the best performance on both datasets, with an F1 score of 75.74% on the MSRS-CD dataset and 91.41% on the LEVIR-CD dataset.

关键词： Feature extraction Remote sensing Transformers Convolutional neural networks change detection algorithms Adaptation models Vectors Shape Semantics Deep learning Attention mechanism change detection dataset (CDD) feature pyramid multiscale change detection remote sensing images

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CGA-Net: A CNN-GAT Aggregation Network Based on Metric for change detection in Remote Sensing

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 8360-8376页

作者： Lin, Huilan Zhao, Chunlei He, Rong Zhu, Ming Jiang, Xin Qin, Yi Gao, Wen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chongqing Jialing Huaguang Optoelect Technol Co Lt Chongqing 400000 Peoples R China BYD Auto Ind Co Ltd Shenzhen 518000 Peoples R China

change detection aims to reveal the changes of specific regions or objects in a time series. Object-level change detection methods are more suitable for existing needs because they can locate and identify changed objects more accurately. Attempting to solve the problems in existing object-level change detection methods, such as ignoring the relationship between dual-branch features, insufficient utilization of feature point information, and unreasonable fusion weight allocation mechanism, this article proposes an object-level change detection network, CGA-Net, based on metric, which combines similarity measurement with the feature extraction and fusion. Specifically, the feature exchange module (FEM) exchanges the feature point information of the dual branches under the guidance of cosine similarity;the feature aggregation module driven by graph attention network (GAT) aggregates features locally and globally using cosine similarity and GAT;the dual-time feature fusion module assigns weights to different parts for fusion based on feature similarity and correlation. The experimental results show that CGA-Net exhibits excellent performance on the LEVIR-CD and WHU-CD datasets. Our method achieves 94.20% and 86.85% in mAP@0.5 on the two datasets, respectively, and 75.12% and 77.39% in mAP@0.5:0.95, respectively, which has a significant improvement compared to both the algorithms based on bounding boxes and the algorithms based on masks. It is fully proven that CGA-Net can effectively improve the accuracy of change detection in different scenarios.

关键词： Feature extraction Object recognition Remote sensing Finite element analysis Data mining Correlation Computer architecture change detection algorithms Accuracy Deep learning Cosine similarity feature exchange feature fusion graph attention object-level change detection

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An Object-Based change detection Method Considering Temporal-Spatial Similarity in Long Time Series

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Chen, Lisu Li, Shanhong Zhu, Enyan Shanghai Maritime Univ Coll Ocean Sci & Engn Shanghai 201306 Peoples R China Ningbo Univ Zhejiang Collaborat Innovat Ctr Land & Marine Spat Ningbo 315211 Peoples R China Shanghai Maritime Univ Coll Transport & Commun Shanghai 201306 Peoples R China

A new object-based change detection method has been proposed to address the limitations of existing research based on pixel-based change detection, as well as the neglect of concurrent changes, pixel spatiotemporal information, and the alteration of boundary information due to changes. Additionally considering the concurrence of changes, the measurement of similarity in time series of adjacent pixels needs to take into account the phase shifting and scaling of the time series. In this study, based on the considerations mentioned above, we first constructed time series using available Landsat 5, 7, and 8 data collected in the study area and used a pixel-based change detection method to obtain change information. Then, considering the heterogeneity within objects, we first combined the change information to extract change seeds. After that, the inside_similarity metric was introduced, which is computed by dynamic time wraping (DTW) algorithm, and it was used to impose sequential constraints on the expansion of seeds. Considering that changes can alter both the interior and boundaries of objects, we applied a conditional judgment to all pixels outside the seeds. Through quantitative assessment in three experimental areas, the method proposed in this article improved producer's accuracy (PA) by 6.9%, 2.7%, and 5.5% and user's accuracy (UA) by 6.1%, 3.1%, and 6.6% with F1-score improved by 6.49%, 2.91%, and 7.45% compared to purely pixel-based change detection methods. Combined with qualitative assessment, the object-based change detection method is proved to increase the accuracy of change detection.

关键词： Time series analysis Accuracy Landsat Data mining Feature extraction Classification algorithms change detection algorithms Remote sensing Image segmentation Urban areas change detection dynamic time wraping (DTW) flood fill object-based

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algorithms for change detection With Sparse Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年第0期68卷 1331-1345页

作者： Jain, Aditi Sarvepalli, Pradeep Bhashyam, Srikrishna Kannu, Arun Pachai Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

We consider the change detection problem where the pre-change observation vectors are purely noise and the post-change observation vectors are noise-corrupted compressive measurements of sparse signals with a common support, measured using a sensing matrix. In general, post-change probability density function (pdf) of the observations depends on parameters such as the support and variances of the sparse signal. When these parameters are unknown, we propose two approaches. In the first approach, we approximate the post-change pdf based on the known parameters such as mutual coherence of the sensing matrix and bounds on the signal variances. In the second approach, we parameterize the post-change pdf with an unknown parameter and try to estimate this parameter using two different methods, stochastic gradient descent and generalized likelihood ratio. In both these approaches, we employ the conventional cumulative sum (CUSUM) algorithm with various decision statistics such as the energy of the observations, correlation values with columns of the sensing matrix and the maximum value of such correlations. We analytically characterize the worst case detection delay and average run length to false alarm performance of pdf-approximation based approach. We also numerically study the performance and offer insights on the relevance of various decision statistics in different signal to noise ratio (SNR) regimes. We also address the problem of designing sensing matrices with small mutual coherence by using designs from quantum information theory. One such design using equi-angular lines has an additional structure which allows exact characterization of the post-change pdf of the correlation values, even when the support set of the sparse signal is unknown. We apply our detection algorithms with sensing matrix designed from equi-angular lines to a massive random access problem and show their superior performance over conventional Gold codes.

关键词： Sensors Sparse matrices Delays change detection algorithms Probability density function detection algorithms Correlation CUSUM algorithm detection delay average run length sensing matrix design mutual coherence quantum information theory

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Lane-Level Map-Aided SLAM Approach for Persistent Positioning in GNSS-Denied Areas

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Yafei Jiang, Wanqi Zhang, Xiaoguo Southeast Univ Sch Instrument Sci & Engn Nanjing 210018 Peoples R China

Accurate positioning is essential for the navigation of autonomous vehicles. Although the Global Navigation Satellite System (GNSS) can provide high-precision positioning capabilities for vehicles in open environments, its positioning capabilities face significant challenges in urban environments with high levels of obstruction and severe multipath effects. In GNSS-denied environments, recursive positioning systems like inertial navigation systems (INSs), LiDAR/vision simultaneous localization and mapping (SLAM), and so on, can provide positioning capabilities, but the error characteristics of their measurement sensors cause the accumulated error of the positioning system to easily diverge over time. Therefore, this article proposes a novel factor graph optimization (FGO) framework for real-time vehicle navigation. This framework integrates visual inertial and map information, which obtains the normal vehicle-to-lane distance between by lane detection in real-time, and adds the measurement constraints of high-definition (HD) vectorized road network maps to the sliding window-based FGO. This joint optimization uses a constructed lane-level map-matching algorithm to achieve the elimination of the cumulative positioning error of the vehicle in GNSS-denied environments. The navigation performance of the proposed algorithm is evaluated through experiments on public datasets and real-world scenarios, and the results show that our method can achieve accurate positioning in situations of continuous GNSS-blocked, relying on vectorized road network maps.

关键词： Simultaneous localization and mapping Roads Accuracy Lane detection Location awareness Global navigation satellite system change detection algorithms Cameras Sensors Odometry Factor graph optimization (FGO) Global Navigation Satellite System (GNSS)-denied environments lane detection normal distance vectorized road network maps vehicle navigation

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