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Deep learning for change detection in remote sensing:a review

Geo-Spatial Information science 2023年第3期26卷 262-288页

作者： Ting Bai Le Wang Dameng Yin Kaimin Sun Yepei Chen Wenzhuo Li Deren Li Project Planning Department Wuhan Land Arranging Storage CenterWuhanChina Department of Geography University at BuffaloThe State University of New YorkBuffaloUSA Institute of Crop Sciences Chinese Academy of Agricultural SciencesBeijingChina State Key Laboratory of Information Engineering in Surveying Mapping and Remote SensingWuhan UniversityWuhanChina School of Computer Science Hubei University of TechnologyWuhanChina Collaborative Innovation Center of Geospatial Technology Wuhan UniversityWuhanChina

A large number of publications have incorporated deep learning in the process of remote sensing change *** these Deep Learning Change Detection(DLCD)publications,deep learning methods have demonstrated their superiority over conventional change detection ***,the theoretical underpinnings of why deep learning improves the performance of change detection remain *** of today,few in-depth reviews have investigated the mechanisms of *** such a review,five critical questions remain *** DLCD provide improved information representation for change detection?If so,how?How to select an appropriate DLCD method and why?How much does each type of change benefits from DLCD in terms of its performance?What are the major limitations of existing DLCD methods and what are the prospects for DLCD?To address these five questions,we reviewed according to the following *** grouped the DLCD information assemblages into the four unique dimensions of remote sensing:spectral,spatial,temporal,and *** the extraction of information in each dimension,the difference between DLCD and conventional change detection methods was *** proposed a taxonomy of existing DLCD methods by dividing them into two distinctive pools:separate and coupled *** advantages,limitations,applicability,and performance were thoroughly investigated and explicitly *** examined the variations in performance between DLCD and conventional change *** depicted two limitations of DLCD,*** sample and hardware and software *** on these analyses,we identified directions for future *** a result of our review,we found that DLCD’s advantages over conventional change detection can be attributed to three factors:improved information representation;improved change detection methods;and performance *** has to surpass the limitations with regard to training samples and computing infrastructure.

关键词： Deep learning change detection remote sensing review information representation

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The Impact of the 2024 Total Solar Eclipse on the Power System Frequency of US Interconnections: A Case Study

The Impact of the 2024 Total Solar Eclipse on the Power Syst...

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IEEE Southeastcon

作者： Saurav Dulal Yan Wen Yilu Liu Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Oak Ridge National Laboratory Oak Ridge TN USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

With the deployment of wide-area monitoring systems, power systems can be continuously monitored and analyzed, particularly during unusual events. Frequency fluctuations in the United States (US) interconnected power grid are influenced by various factors, including weather conditions and variability in solar power generation. This paper investigates the impact of the solar eclipse on April 8, 2024, on the frequency stability of the US power grid. Phasor measurement data from multiple interconnections are collected and analyzed. Methods of approximate frequency estimation for each interconnection are employed. The study involves comparing the frequency distribution, probability density, and variance on the eclipse day with those of non-eclipse days. The results provide insight into the grid's response to reduced solar generation and underscore the importance of predictive measures to maintain stability during solar eclipses and similar disruptions. The study shows that the Electric Reliability Council of Texas (ERCOT) experienced a dramatic increase in frequency fluctuations − 198% higher compared to periods without eclipse - highlighting the grid's vulnerability to reduced solar irradiance. Conversely, Eastern Interconnection (EI) demonstrated remarkable stability during the eclipse with minimal frequency fluctuations.

关键词： Solar eclipses Fluctuations Power system stability Stability analysis Power grids Solar power generation Thermal stability Monitoring Resilience Meteorology

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Optimal Coherent Quantum Phase Estimation via Tapering

arXiv

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arXiv 2024年

作者： Patel, Dhrumil Tan, Shi Jie Samuel Subaşı, Yiğit Sornborger, Andrew T. Department of Computer Science Cornell University IthacaNY14850 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD United States Department of Computer Science University of Maryland College ParkMD United States Information Sciences Los Alamos National Laboratory Los AlamosNM United States

Quantum phase estimation is one of the fundamental primitives that underpins many quantum algorithms, including Shor’s algorithm for efficiently factoring large numbers. Due to its significance as a subroutine, in this work, we consider the coherent version of the phase estimation problem, where given an arbitrary input state and black-box access to unitaries U and controlled-U, the goal is to estimate the phases of U in superposition. Most existing phase estimation algorithms involve intermediary measurements that disrupt coherence. Only a couple of algorithms, including the standard quantum phase estimation algorithm, consider this coherent setting. However, the standard algorithm only succeeds with a constant probability. To boost this success probability, it employs the coherent median technique, resulting in an algorithm with optimal query complexity (the total number of calls to U and controlled-U). However, this coherent median technique requires a large number of ancilla qubits and a computationally expensive quantum sorting network. To address this, in this work, we propose an improved version of this standard algorithm called the tapered quantum phase estimation algorithm. It leverages tapering/window functions commonly used in signal processing. Our algorithm achieves the optimal query complexity without requiring the expensive coherent median technique to boost success probability. We also show that the tapering functions that we use are optimal by formulating optimization problems with different optimization criteria. Beyond the asymptotic regime, we also provide non-asymptotic query complexity of our algorithm, as it is crucial for practical implementation. Finally, we propose an efficient algorithm to prepare the quantum state corresponding to the optimal tapering function. © 2024, CC BY.

关键词： Subroutines

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Robust Training of Deep Learning Models for Mammogram Classification

Robust Training of Deep Learning Models for Mammogram Classi...

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IEEE International Symposium on Biomedical Imaging

作者： Josué Martínez-Martínez Olivia Brown Jun Bai Sheida Nabavi School of Computing University of Connecticut Storrs USA MIT Lincoln Laboratory Lexington USA Department of Computer Science University of Cincinnati Cincinnati USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Deep neural networks are increasingly applied in healthcare to detect and diagnose medical conditions, including breast cancer through mammogram analysis. However, these models are vulnerable to adversarial attacks and distribution shifts, undermining diagnostic reliability and trust. This study explores baseline methods to address these vulnerabilities and proposes a modified robust training approach to enhance model robustness and reliability in mammogram analysis.

关键词： Training Measurement Training data Robustness Mammography Breast cancer Standards Medical diagnostic imaging X-ray imaging Resilience

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Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

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Space(science & Technology) 2023年第1期3卷 389-401页

作者： Bin Jiang Yingchun Yan Jingjing Zhao Xiang Xiao Li You Di Zhang Jizhao Lei Kezhi Wang Wenjin Wang Xiqi Gao National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China Purple Mountain Laboratories Nanjing 211100China Department of Electronic Information Engineering Beihang UniversityBeijing 100191China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China China Aerospace Science and Technology Corporation Beijing 100094China Department of Computer Science Brunel University LondonUxbridgeMiddlesexUB83PHUK.

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance *** satellite communication systems,channel acquisition and energy consumption are two critical *** this end,we investigate the tradeoff between the total energy efficiency(TEE)and minimum EE(MEE)for robust multigroup multicast satellite communication systems in this ***,under the total power constraint,we investigate the robust beamforming aimed at balancing the TEE-MEE,so as to achieve the balance between the fairness and total performance on the system *** this optimization problem,we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user ***,the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex *** addition,depending on the solution rank value,Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible ***,simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE,thus realizing a tradeoff between fairness and system EE *** is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

关键词： convex eigenvalue sequentially

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Multi-agent policy transfer via task relationship modeling

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science China(Information sciences) 2024年第8期67卷 102-114页

作者： Rongjun QIN Feng CHEN Tonghan WANG Lei YUAN Xiaoran WU Yipeng KANG Zongzhang ZHANG Chongjie ZHANG Yang YU National Key Laboratory for Novel Software Technology Nanjing University Polixir Technologies School of Engineering and Applied Sciences Harvard University Department of Computer Science and Technology Tsinghua University Institute for Interdisciplinary Information Sciences Tsinghua University

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous studies on multi-agent transfer learning have accommodated teams of different sizes but heavily relied on the generalization ability of neural networks for adapting to unseen tasks. We posit that the relationship among tasks provides key information for policy *** utilize this relationship for efficient transfer by attempting to discover and exploit the knowledge among tasks from different teams, proposing to learn an effect-based task representation as a common latent space among tasks, and using it to build an alternatively fixed training scheme. Herein, we demonstrate that task representation can capture the relationship among teams and generalize to unseen tasks. Thus, the proposed method helps transfer the learned cooperation knowledge to new tasks after training on a few source ***, the learned transferred policies help solve tasks that are difficult to learn from scratch.

关键词： multi-agent reinforcement learning cooperative transfer learning task relationship modeling multi-agent policy reuse multi-agent multi-task learning

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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Surface Code Error Correction with Crosstalk Noise

arXiv

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arXiv 2025年

作者： Zhou, Zeyuan Ji, Andrew Ding, Yongshan Department of Computer Science Yale University Yale Quantum Institute New HavenCT United States University of Chicago Laboratory Schools ChicagoIL United States Department of Computer Science Department of Applied Physics Yale University Yale Quantum Institute New HavenCT United States

The design and performance analysis of quantum error correction (QEC) codes are often based on incoherent and independent noise models since it is easy to simulate. However, these models fail to capture realistic hardware noise sources, such as correlated errors (crosstalk), which can significantly impact QEC code performance, especially when they occur between data and ancillary qubits. In this paper, we systematically study various types of crosstalk noise and quantify their effects on surface codes through memory and stability experiments. Based on our findings, we introduce crosstalk-robust implementations of QEC via flag qubit designs and redundant stabilizer checks. We perform both numerical and analytical studies to demonstrate the efficacy of these strategies. In addition, we analyze logical crosstalk in an [[n, k > 1, d]] code block and establish analytical conditions under which physical crosstalk does not lead to logical crosstalk. Together, our analytical and numerical results shed light on designing QEC codes that are robust against hardware realistic crosstalk noise, paving the way for reliable experimental realization of fault-tolerant quantum computing. © 2025, CC BY.

关键词： Quantum channel

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A Survey on Distributed Frameworks for Machine Learning Based Big Data Analysis 21

A Survey on Distributed Frameworks for Machine Learning Base...

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21st International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2022

作者： Haddad, Omar Fkih, Fethi Omri, Mohamed Nazih Mars Research Laboratory LR17ES05 University of Sousse Sousse Tunisia Faculty of Economics and Management of Sfax University of Sfax Sfax Tunisia Department of Computer Science College of Computer Qassim University Buraydah51452 Saudi Arabia

ISBN: (纸本)9781643683164

The rapid pace of technological progress has led to an increasing growth in the volume of digital data circulating on servers and on the web. This has contributed to the birth of the concept of Big Data. Simply put, this concept refers to the huge amount of information on the Internet;yet it also reveals the heterogeneity and complexity of such data. Therefore, analyzing these data, especially unstructured data, has become important since they can be used in many areas such as company management, health, smart city. In order to analyze these data, novel efficient tools are required as the current ones are not effective enough. This paper surveys the most frequently used tools and platforms for Big Data analysis with due emphasis on Machine Learning-based models. The results of this study provide in-depth knowledge of Big Data analytics applications related to machine learning that can contribute to the innovation and development of big data analytics platforms. Moreover, it helps to choose the right tools to ensure the best performance for designing an analytics system. © 2022 The authors and IOS Press. All rights reserved.

关键词： Data Analytics

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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