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5,645 results

1. A Comparative Study of the Impact of Different First Order Optimizers on the Learning Process of UNet for Change Detection Task

Author

Dokkar, Basma, Meddour, Bouthaina, Bouanane, Khadra, Allaoui, Mebarka, Kherfi, Mohamed Lamine, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bennour, Akram, editor, Bouridane, Ahmed, editor, and Chaari, Lotfi, editor

Published
2024
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3. Land Cover Change Detection Using Multi-spectral Satellite Images

Author

Yagnesh, Galla, Jagapathi, Mare, Sri Lekha, Kolasani Sai, Reddy, Duddugunta Bharath, Pavan Kumar, C. S., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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4. An Error-Based Measure for Concept Drift Detection and Characterization

Author

Bugnicourt, Antoine, Mokadem, Riad, Morvan, Franck, Bebeshina, Nadia, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sellmann, Meinolf, editor, and Tierney, Kevin, editor

Published
2023
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5. Change Detection Using Multispectral Images for Agricultural Application

Author

Lasya, M., Phanitha Sai Lakshmi, V., Anjum, Tahaseen, Vaddi, Radhesyam, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Das, Swagatam, editor, Saha, Snehanshu, editor, Coello Coello, Carlos A., editor, and Bansal, Jagdish Chand, editor

Published
2023
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6. Decentralized Vision-Based Byzantine Agent Detection in Multi-robot Systems with IOTA Smart Contracts

Author

Salimpour, Sahar, Keramat, Farhad, Queralta, Jorge Peña, Westerlund, Tomi, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jourdan, Guy-Vincent, editor, Mounier, Laurent, editor, Adams, Carlisle, editor, Sèdes, Florence, editor, and Garcia-Alfaro, Joaquin, editor

Published
2023
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7. Fuel Break Monitoring with Sentinel-2 Imagery and GEDI Validation

Author

Pereira-Pires, João E., Aubard, Valentine, Baldassarre, G., Fonseca, José M., Silva, João M. N., Mora, André, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Camarinha-Matos, Luis M., editor, Heijenk, Geert, editor, Katkoori, Srinivas, editor, and Strous, Leon, editor

Published
2022
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8. Estimating Change Intensity and Duration in Human Activity Recognition Using Martingales

Author

Etumusei, Jonathan, Carracedo, Jorge Martinez, McClean, Sally, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Jansen, Giorgio, editor, Pardalos, Panos M., editor, Giuffrida, Giovanni, editor, and Umeton, Renato, editor

Published
2022
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9. AAUNet: An Attention Augmented Convolution Based UNet for Change Detection in High Resolution Satellite Images

Author

Patil, P. S., Holambe, R. S., Waghmare, L. M., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Raman, Balasubramanian, editor, Murala, Subrahmanyam, editor, Chowdhury, Ananda, editor, Dhall, Abhinav, editor, and Goyal, Puneet, editor

Published
2022
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10. Siamese Networks with Transfer Learning for Change Detection in Sentinel-2 Images

Author

Andresini, Giuseppina, Appice, Annalisa, Dell’Olio, Domenico, Malerba, Donato, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bandini, Stefania, editor, Gasparini, Francesca, editor, Mascardi, Viviana, editor, Palmonari, Matteo, editor, and Vizzari, Giuseppe, editor

Published
2022
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11. Mars Surface Multi-decadal Change Detection Using ISRO’s Mars Color Camera (MCC) and Viking Orbiter Images

Author

Misra, Indranil, Rohil, Mukesh Kumar, Manthira Moorthi, S., Dhar, Debajyoti, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Satish Kumar, editor, Roy, Partha, editor, Raman, Balasubramanian, editor, and Nagabhushan, P., editor

Published
2021
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12. Unsupervised Change Detection in Remote Sensing Images Using CNN Based Transfer Learning

Author

Paul, Josephina, Shankar, B. Uma, Bhattacharyya, Balaram, Datta, Alak Kumar, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Singh, Mayank, editor, Tyagi, Vipin, editor, Gupta, P. K., editor, Flusser, Jan, editor, Ören, Tuncer, editor, and Sonawane, V. R., editor

Published
2021
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13. Multi-scale Convolutional Neural Network for Remote Sensing Image Change Detection

Author

Yu, Xiao, Fan, Junfu, Zhang, Peng, Han, Liusheng, Zhang, Dafu, Sun, Guangwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Xie, Yichun, editor, Li, Yong, editor, Yang, Ji, editor, Xu, Jianhui, editor, and Deng, Yingbin, editor

Published
2020
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14. Dilated CNN Based Human Verifier for Intrusion Detection

Author

Shahbaz, Ajmal, Jo, Kang-Hyun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Ohyama, Wataru, editor, and Jung, Soon Ki, editor

Published
2020
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16. Change Detection and Blob Tracking of Fish in Underwater Scenarios

Author

Radolko, Martin, Farhadifard, Fahimeh, von Lukas, Uwe, Barbosa, Simone Diniz Junqueira, Series Editor, Filipe, Joaquim, Series Editor, Kotenko, Igor, Series Editor, Sivalingam, Krishna M., Series Editor, Washio, Takashi, Series Editor, Yuan, Junsong, Series Editor, Zhou, Lizhu, Series Editor, Ghosh, Ashish, Series Editor, Cláudio, Ana Paula, editor, Bechmann, Dominique, editor, Richard, Paul, editor, Yamaguchi, Takehiko, editor, Linsen, Lars, editor, Telea, Alexandru, editor, Imai, Francisco, editor, and Tremeau, Alain, editor

Published
2019
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17. Experimenting with Routes of Different Geometric Complexity in the Context of Urban Road Environment Detection from Traffic Sign Data

Author

Fazekas, Zoltán, Balázs, Gábor, Gerencsér, László, Gáspár, Péter, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, and Sierpiński, Grzegorz, editor

Published
2019
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19. Remote Sensing Image Change Detection Algorithm Based on BM3D and PCANet

Author

Wan, Yi, Liu, Yongjin, Peng, Qunnie, Jie, Feiran, Ming, Delie, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Yongtian, editor, Huang, Qingmin, editor, and Peng, Yuxin, editor

Published
2019
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20. Hierarchical Decision Tree for Change Detection Using High Resolution Remote Sensing Images

Author

Xie, Zhiwei, Wang, Min, Han, Yaohui, Yang, Dayong, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Xie, Yichun, editor, Zhang, Anbing, editor, Liu, Haixin, editor, and Feng, Lili, editor

Published
2019
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21. Interpreting Intermittent Bugs in Mozilla Applications Using Change Angle

Author

Huang, David Tse Jung, Koh, Yun Sing, Dobbie, Gillian, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sivalingam, Krishna M., Founding Editor, Washio, Takashi, Founding Editor, Yuan, Junsong, Founding Editor, Islam, Rafiqul, editor, Koh, Yun Sing, editor, Zhao, Yanchang, editor, Warwick, Graco, editor, Stirling, David, editor, Li, Chang-Tsun, editor, and Islam, Zahidul, editor

Published
2019
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22. A Dynamic Network Change Detection Method Using Network Embedding

Author

Sun, Tong, Liu, Yan, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Sun, Xingming, editor, Pan, Zhaoqing, editor, and Bertino, Elisa, editor

Published
2018
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23. An Unsupervised Change Detection Approach for Remote Sensing Image Using Visual Attention Mechanism

Author

Wu, Lin, Feng, Guanghua, Long, Jiangtao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhao, Yao, editor, Kong, Xiangwei, editor, and Taubman, David, editor

Published
2017
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24. Using the Cumulative Sum Algorithm Against Distributed Denial of Service Attacks in Internet of Things

Author

Machaka, Pheeha, McDonald, Andre, Nelwamondo, Fulufhelo, Bagula, Antoine, Akan, Ozgur, Series editor, Cao, Jiannong, Series editor, Coulson, Geoffrey, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Bellavista, Paolo, Series editor, Vinh, Phan Cong, editor, and Alagar, Vangalur, editor

Published
2016
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25. Change Detection in Remote Sensing Images Based on Clonal Selection Algorithm

Author

Wu, Tao, Lei, Yu, Gong, Maoguo, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Gong, Maoguo, editor, Pan, Linqiang, editor, Song, Tao, editor, and Zhang, Gexiang, editor

Published
2016
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26. DNN-Based Joint Classification for Multi-source Image Change Detection

Author

Ma, Wenping, Li, Zhizhou, Zhang, Puzhao, Hu, Tianyu, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Gong, Maoguo, editor, Pan, Linqiang, editor, Song, Tao, editor, and Zhang, Gexiang, editor

Published
2016
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27. Surpassing Entertainment with Computer Games: Online Tools for Early Warnings of Mild Cognitive Impairment

Author

Pataki, Béla, Hanák, Péter, Csukly, Gábor, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Liu, Ting, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Helfert, Markus, editor, Holzinger, Andreas, editor, Ziefle, Martina, editor, Fred, Ana, editor, O'Donoghue, John, editor, and Röcker, Carsten, editor

Published
2015
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28. Multiscale Change Detection Method for Remote Sensing Images Based on Online Learning Framework

Author

Zhang, Jianlong, Zhai, Jianfeng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, He, Xiaofei, editor, Gao, Xinbo, editor, Zhang, Yanning, editor, Zhou, Zhi-Hua, editor, Liu, Zhi-Yong, editor, Fu, Baochuan, editor, Hu, Fuyuan, editor, and Zhang, Zhancheng, editor

Published
2015
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29. Monitoring Health of Artificial Robinia Pseudoacacia Changes in the Yellow River Delta by the Analysis of Multiyear NDWI Data

Author

Yao, Ling, Liu, Qingsheng, Liu, Gaohuan, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Kotenko, Igor, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bian, Fuling, editor, and Xie, Yichun, editor

Published
2016
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30. Change Detection of Cities

Author

Pédrinis, F., Morel, M., Gesquière, G., Cartwright, William, Series editor, Gartner, Georg, Series editor, Meng, Liqiu, Series editor, Peterson, Michael P, Series editor, Breunig, Martin, editor, Al-Doori, Mulhim, editor, Butwilowski, Edgar, editor, Kuper, Paul V., editor, Benner, Joachim, editor, and Haefele, Karl Heinz, editor

Published
2015
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33. Change Detection in CityGML Documents

Author

Redweik, Richard, Becker, Thomas, Cartwright, William, Series editor, Gartner, Georg, Series editor, Meng, Liqiu, Series editor, Peterson, Michael P, Series editor, Breunig, Martin, editor, Al-Doori, Mulhim, editor, Butwilowski, Edgar, editor, Kuper, Paul V., editor, Benner, Joachim, editor, and Haefele, Karl Heinz, editor

Published
2015
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34. A Hybrid Approach Integrating 3D City Models, Remotely Sensed SAR Data and Interval-Valued Fuzzy Soft Set Based Decision Making for Post Disaster Mapping of Urban Areas

Author

Ali, Iftikhar, Khan, Aftab Ahmed, Qureshi, Salman, Umar, Mudassar, Haase, Dagmar, Hijazi, Ihab, Cartwright, William, Series editor, Gartner, Georg, Series editor, Meng, Liqiu, Series editor, Peterson, Michael P, Series editor, Breunig, Martin, editor, Al-Doori, Mulhim, editor, Butwilowski, Edgar, editor, Kuper, Paul V., editor, Benner, Joachim, editor, and Haefele, Karl Heinz, editor

Published
2015
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35. Cumulative Sum Algorithms for Automatic Detection of Gas Well Parameter Changes * *This paper is partly supported by by The Ministry of Education and Science of the Russian Federation, Goszadanie No 2.3208.2017/PCh and by Russian Foundation for Basic Research Grant 16-01-00121 A

Author

E. Frantcuzskaia, Sergey E. Vorobeychikov, Anton Kudinov, and Yu. B. Burkatovskaya

Subjects
0209 industrial biotechnology, Engineering, Sequence, business.industry, 0211 other engineering and technologies, 02 engineering and technology, Variance (accounting), Task (project management), 020901 industrial engineering & automation, Distribution function, Distribution (mathematics), Control and Systems Engineering, 021105 building & construction, Point (geometry), business, Random variable, Algorithm, Change detection
Abstract

The problem of the change point detection in a sequence of random variables is considered. The task arises in control of technological processes, particularly, in oil and gas production management. Some equipment parameters are to be controlled in order to detect a change of the equipment characteristics and, consequently, a breakdown of its technological regime. As a rule, the data observed are stochastic with the unknown distribution. In the paper a non-parametric method for detection a change in the mean or in the variance of data is developed and some modifications are proposed. All the algorithms do not use the information concerning the distribution function of observations before and after the change point. The algorithms are applied to detect change points of the characteristics of a gas well.

Published
2017

36. Online optimization in the Non-Stationary Cloud: Change Point Detection for Resource Provisioning (Invited Paper)

Author

Zhenhua Liu, Joshua Comden, and Jessica Maghakian

Subjects
Computer science, business.industry, Distributed computing, Big data, Control (management), 020206 networking & telecommunications, Provisioning, Cloud computing, 02 engineering and technology, Resource (project management), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Spike (software development), Online algorithm, business, Change detection
Abstract

The rapid mainstream adoption of cloud computing and the corresponding spike in the energy usage of big data systems make the efficient management of cloud computing resources a more pressing issue than ever before. To this end, numerous online algorithms such as Receding Horizon Control and Online Balanced Descent have been designed. However it is difficult for cloud service providers to select the best control algorithm dynamically for resource provisioning when confronted with consumer resource demands that are notoriously unpredictable and volatile. Furthermore, it highly possible that it might not be the case for any one algorithm to consistently perform well over the months-long contract period. In this paper, we first exemplify the need to address non-stationarity in cloud computing by showcasing traces from MS Azure. We then develop a novel meta-algorithm that combines change point detection and online optimization. The new algorithm is shown to outperform existing solutions in real-world trace-driven simulations.

Published
2019

37. 〈Original Papers〉Spatial context learning in change detection task

Subjects
visual context, contextual cueing, attentional setting, change detection
Abstract

Visual context, such as a spatial relationship between the locations of a particular target object and the other distractor objects, is learned with repeated presentations of the same spatial layout, and facilitates searching for or detecting the target object. This context learning is referred to as contextual cueing. Previous studies have demonstrated that learned context involved the association target location with individual locations of distractors （nonconfigural learning） and the association target location with the overall distractor configuration （configural learning） in visual search and change detection tasks respectively, indicating that the content of context learning was task specific. The purpose of the present study was to examine whether the content of context learning relied on participants’ attentional control setting during performing change detection task. The results showed that nonconfigural learning tended to occur even in change detection task when it was necessary for detecting the target effectively to direct spatial attention to individual distractors, suggesting the possibility that the aspect of context learning is dependent on participants’ attentional setting., 専攻: 認知心理学

Published
2016

38. A Fast Detection Algorithm for Change Detection in National Forestland "One Map" Based on NLNE Quad-Tree.

Author

Gao, Fei, Su, Xiaohui, Chen, Yuling, Wu, Baoguo, Tian, Yingze, Zhang, Wenjie, and Li, Tao

Subjects
FORESTS & forestry, FOREST management, GEOGRAPHIC information systems, VECTOR data, MOUNTAIN forests, ALGORITHMS
Abstract

The National Forestland "One Map" applies the boundaries and attributes of sub-elements to mountain plots by means of spatial data to achieve digital management of forest resources. The change detection and analysis of forest space and property is the key to determining the change characteristics, evolution trend and management effectiveness of forest land. The existing spatial overlay method, rasterization method, object matching method, etc., cannot meet the requirements of high efficiency and high precision at the same time. In this paper, we investigate a fast algorithm for the detection of changes in "One Map", taking Sichuan Province as an example. The key spatial characteristic extraction method is used to uniquely determine the sub-compartments. We construct an unbalanced quadtree based on the number of maximum leaf node elements (NLNE Quad-Tree) to narrow down the query range of the target sub-compartments and quickly locate the sub-compartments. Based on NLNE Quad-Tree, we establish a change detection model for "One Map" (NQT-FCDM). The results show that the spatial feature combination of barycentric coordinates and area can ensure the spatial uniqueness of 44.45 million sub-compartments in Sichuan Province with 1 m~0.000001 m precision. The NQT-FCDM constructed with 1000–6000 as the maximum number of leaf nodes has the best retrieval efficiency in the range of 100,000–500,000 sub-compartments. The NQT-FCDM shortens the time by about 75% compared with the traditional spatial union analysis method, shortens the time by about 50% compared with the normal quadtree and effectively solves the problem of generating a large amount of intermediate data in the spatial union analysis method. The NQT-FCDM proposed in this paper improves the efficiency of change detection in "One Map" and can be generalized to other industries applying geographic information systems to carry out change detection, providing a basis for the detection of changes in vector spatial data. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review.

Author

Cheng, Guangliang, Huang, Yunmeng, Li, Xiangtai, Lyu, Shuchang, Xu, Zhaoyang, Zhao, Hongbo, Zhao, Qi, and Xiang, Shiming

Subjects
DEEP learning, FEATURE extraction, LAND cover, REMOTE sensing, AGRICULTURAL surveys, OPTICAL remote sensing
Abstract

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task. [ABSTRACT FROM AUTHOR]

Published
2024
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40. TDSCCNet: twin-depthwise separable convolution connect network for change detection with heterogeneous images.

Author

Min Wang, Liang Huang, Bo-Hui Tang, Weipeng Le, and Qiuyuan Tian

Subjects
FEATURE extraction, OPTICAL sensors, OPTICAL images, REMOTE sensing, DEEP learning
Abstract

The task of change detection (CD) in optical and SAR images is an ever-evolving and demanding subject within the realm of remote sensing (RS). It holds great significance to identify the target areas by using complementary information between the two. Due to the distinct imaging mechanisms employed by optical and SAR sensors, effectively and accurately identifying changing regions can be challenging. To this end, a novel heterogeneous RS images CD network (Twin-Depthwise Separable Convolution Connect Network, TDSCCNet) is proposed in this paper. Image domain transformation is a front-end task, while the back-end employs a single-branch bilayer depthwise separable convolutionconnected encoder-decoder to accomplish CD work. Specifically, first, the cycle-consistent adversarial network (CycleGAN) serves to integrate the optical and SAR visual domains, and a consistent feature expression is obtained. Second, the single-branch encoder structure of bilayer depthwise separable convolution is employed to realize change feature extraction. Finally, the multiscale connected decoder reconstructed by change map is utilized to reconstruct the original images and solve the local discontinuities and holes in the binary change map. Multiscale loss is designated for optimizing the global and local effects to alleviate the class imbalance problem. It was tested on four representative datasets from Gloucester, Shuguang Village, Italy and WV-3 datasets with an overall accuracy of 97.36%, 97.01%, 97.62%, and 98.01% respectively. By comparing the existing methods, experimental results confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
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45. A Review of Crowdsourcing Update Methods for High-Definition Maps.

Author

Guo, Yuan, Zhou, Jian, Li, Xicheng, Tang, Youchen, and Lv, Zhicheng

Subjects
CROWDSOURCING, GEOGRAPHICAL perception, DATA mining, MAPS, AUTONOMOUS vehicles, RESEARCH personnel
Abstract

High-definition (HD) maps serve as crucial infrastructure for autonomous driving technology, facilitating vehicles in positioning, environmental perception, and motion planning without being affected by weather changes or sensor-visibility limitations. Maintaining precision and freshness in HD maps is paramount, as delayed or inaccurate information can significantly impact the safety of autonomous vehicles. Utilizing crowdsourced data for HD map updating is widely recognized as a superior method for preserving map accuracy and freshness. Although it has garnered considerable attention from researchers, there remains a lack of comprehensive exploration into the entire process of updating HD maps through crowdsourcing. For this reason, it is imperative to review and discuss crowdsourcing techniques. This paper aims to provide an overview of the overall process of crowdsourced updates, followed by a detailed examination and comparison of existing methodologies concerning the key techniques of data collection, information extraction, and change detection. Finally, this paper addresses the challenges encountered in crowdsourced updates for HD maps. [ABSTRACT FROM AUTHOR]

Published
2024
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47. MtSCCD：面向深度学习的土地利用场景分类与变化检测数据集.

Author

周, 维勋, 刘, 京雷, 彭, 代锋, 管, 海燕, and 邵, 振峰

Abstract

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Published
2024
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48. Dynamic Operation Optimization of Complex Industries Based on a Data-Driven Strategy.

Author

Tian, Huixin, Zhao, Chenning, Xie, Jueping, and Li, Kun

Subjects
OPTIMIZATION algorithms, MANUFACTURING processes, MATHEMATICAL optimization, TIME series analysis, PROBLEM solving
Abstract

As industrial practices continue to evolve, complex process industries often exhibit characteristics such as multivariate correlation, dynamism, and nonlinearity, making traditional mechanism modeling inadequate in terms of addressing the intricacies of complex industrial problems. In recent years, with advancements in control theory and industrial practices, there has been a substantial increase in the volume of industrial data. Data-driven dynamic operation optimization techniques have emerged as effective solutions for handling complex industrial processes. By responding to dynamic environmental changes and utilizing advanced optimization algorithms, it is possible to achieve dynamic operational optimization in industrial processes, thereby reducing costs and emissions, improving efficiency, and increasing productivity. This correlates nicely with the goals set forth by conventional process operation optimization theories. Nowadays, this dynamic, data-driven strategy has shown significant potential in complex process industries characterized by multivariate correlations and nonlinear behavior. This paper approaches the subject from a data-driven perspective by establishing dynamic optimization models for complex industries and reviewing the state-of-the-art time series forecasting models to cope with changing objective functions over time. Meanwhile, aiming at the problem of concept drift in time series, this paper summarizes new concept drift detection methods and introduces model update methods to solve this challenge. In addressing the problem of solving dynamic multi-objective optimization problems, the paper reviews recent developments in dynamic change detection and response methods while summarizing commonly used as well as the latest performance measures for dynamic multi-objective optimization problems. In conclusion, a discussion of the research progress and challenges in the relevant domains is undertaken, followed by the proposal of potential directions for future research. This review will help to deeply understand the importance and application prospects of data-driven dynamic operation optimization in complex industrial fields. [ABSTRACT FROM AUTHOR]

Published
2024
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49. An Experimental Investigation for Detection, Localization, and Quantification of Compound Changes in Complex Uncertain Systems.

Author

Abdelbarr, Mohamed H., Hernandez-Garcia, Miguel R., Caffrey, John P., and Masri, Sami F.

Subjects
STRUCTURAL health monitoring, UNCERTAIN systems, PREDICATE calculus, LOCALIZATION (Mathematics), COMPLEX compounds, SYSTEM dynamics, SYSTEM identification, STRUCTURAL engineering
Abstract

The field of (practical) data-driven approaches that utilize the vibration signature of target systems for developing mathematical models (for computational purposes, control, or anomaly detection for structural health monitoring) is still an active research area, despite the fact that several powerful system identification techniques have been developed in the system dynamics field to analyze such measurements. However, there is still a paucity of comprehensive experimental studies that investigate the range of validity of such identification techniques, particularly those applicable to realistic situations encountered in the structural engineering field, with the focus on detecting, quantifying, locating, and classifying observed changes, especially when there are significant inherent nonlinearities in the reference (undamaged) complex target structure, and where there are unavoidable sources of errors and uncertainties in the measurements and the attendant data analysis procedures. The research team constructed a well-instrumented, reconfigurable test apparatus (resembling a tall building) that allows the introduction of quantifiable levels of composite changes at various locations, orientations, and types of linear and/or nonlinear changes, with the aim of investigating a subset of the aforementioned challenges facing researchers who are interested in assessing the utility of some practical system identification approaches. The primary focus of the identification approach is on a decomposition procedure that is ideally suited for certain types of structures that possess some topological features that can be exploited to enhance the detectability of small changes. A companion paper provides a detailed description of the testbed features and its instrumentation. The present paper focuses on the analysis of some of the very extensive data sets that were created to study the usefulness of some practical dimensionless probabilistic measures that not only provide normalized change indices but also simultaneously attach a confidence level to each of these indices. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Flood Mapping of Synthetic Aperture Radar (SAR) Imagery Based on Semi-Automatic Thresholding and Change Detection.

Author

Lang, Fengkai, Zhu, Yanyin, Zhao, Jinqi, Hu, Xinru, Shi, Hongtao, Zheng, Nanshan, and Zha, Jianfeng

Subjects
SYNTHETIC aperture radar, SPECKLE interference, URBAN renewal, WATERSHEDS, BACKSCATTERING
Abstract

Synthetic aperture radar (SAR) technology has become an important means of flood monitoring because of its large coverage, repeated observation, and all-weather and all-time working capabilities. The commonly used thresholding and change detection methods in emergency monitoring can quickly and simply detect floods. However, these methods still have some problems: (1) thresholding methods are easily affected by low backscattering regions and speckle noise; (2) changes from multi-temporal information include urban renewal and seasonal variation, reducing the precision of flood monitoring. To solve these problems, this paper presents a new flood mapping framework that combines semi-automatic thresholding and change detection. First, multiple lines across land and water are drawn manually, and their local optimal thresholds are calculated automatically along these lines from two ends towards the middle. Using the average of these thresholds, the low backscattering regions are extracted to generate a preliminary inundation map. Then, the neighborhood-based change detection method combined with entropy thresholding is adopted to detect the changed areas. Finally, pixels in both the low backscattering regions and the changed regions are marked as inundated terrain. Two flood datasets, one from Sentinel-1 in the Wharfe and Ouse River basin and another from GF-3 in Chaohu are chosen to verify the effectiveness and practicality of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
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