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Journal articles on the topic 'Change detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Yang, Le, Yiming Chen, Shiji Song, Fan Li, and Gao Huang. "Deep Siamese Networks Based Change Detection with Remote Sensing Images." Remote Sensing 13, no. 17 (2021): 3394. http://dx.doi.org/10.3390/rs13173394.

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Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection datas

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Javed, Aisha, Sejung Jung, Won Hee Lee, and Youkyung Han. "Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index." Remote Sensing 12, no. 18 (2020): 2952. http://dx.doi.org/10.3390/rs12182952.

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Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a m

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Kennette, Lynne N., Lee H. Wurm, and Lisa R. Van Havermaet. "Change detection." Mental Lexicon 5, no. 1 (2010): 47–86. http://dx.doi.org/10.1075/ml.5.1.03ken.

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A version of the change-detection paradigm was used to examine Good-Enough Representation (Ferreira, Bailey, & Ferraro, 2002). Participants read sentence pairs where a subject noun (e.g., flower) could change to a Superordinate (e.g., plant), Subordinate (e.g., rose), or an Unrelated (e.g., prince) noun. The task was completed cross-linguistically for bilinguals, where the first sentence appeared in English (L1) and the second in French (L2). Linguistic focus was also manipulated. Change detection was extremely high in all conditions in the monolingual sample. In the bilingual sample, focu

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Rensink, Ronald A. "Change Detection." Annual Review of Psychology 53, no. 1 (2002): 245–77. http://dx.doi.org/10.1146/annurev.psych.53.100901.135125.

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Politz, Florian, Monika Sester, and Claus Brenner. "Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information." AGILE: GIScience Series 2 (June 4, 2021): 1–14. http://dx.doi.org/10.5194/agile-giss-2-10-2021.

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Abstract. Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As

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Tse, P. U., D. L. Sheinberg, and N. K. Logothetis. "Attentional Enhancement Opposite a Peripheral Flash Revealed Using Change Blindness." Psychological Science 14, no. 2 (2003): 91–99. http://dx.doi.org/10.1111/1467-9280.t01-1-01425.

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We describe a new method for mapping spatial attention that reveals a pooling of attention in the hemifield opposite a peripheral flash. Our method exploits the fact that a brief full-field blank can interfere with the detection of changes in a scene that occur during the blank. Attending to the location of a change, however, can overcome this change blindness, so that changes are detected. The likelihood of detecting a new element in a scene therefore provides a measure of the occurrence of attention at that element's location. Using this measure, we mapped how attention changes in response t

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Perry, Kimberly, Matthew Pacailler, and Mark W. Scerbo. "The Impact of Natural Visual Interruptions and Cueing on Detecting Changes in Dynamic Scenes." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (2022): 1240–44. http://dx.doi.org/10.1177/1071181322661398.

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The goal of the present study was to examine how naturalistic interruptions (head turns) and cueing affect change detection within dynamic scenes. Based on the memory for goals (Altmann & Trafton, 2002) and visual memory theories (Hollingsworth & Henderson, 2001), participants monitoring videos were expected to detect fewer target changes when interrupted than without interruptions. Additionally, reliable cues that provided information about the target were expected to improve target detection compared to neutral cues (Logan, 1996; Posner, Snyder, & Davidson, 1980). Undergraduate s

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Cho, Kyusik, Dong Yeop Kim, and Euntai Kim. "Zero-Shot Scene Change Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 3 (2025): 2509–17. https://doi.org/10.1609/aaai.v39i3.32253.

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We present a novel, training-free approach to scene change detection. Our method leverages tracking models, which inherently perform change detection between consecutive frames of video by identifying common objects and detecting new or missing objects. Specifically, our method takes advantage of the change detection effect of the tracking model by inputting reference and query images instead of consecutive frames. Furthermore, we focus on the content gap and style gap between two input images in change detection, and address both issues by proposing adaptive content threshold and style bridgi

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Bhavani, M., V. Hanifar Sangeetha, K. Kalaivani, K. Ulagapriya, and A. Saritha. "Change detection algorithm for multi-temporal satellite images: a review." International Journal of Engineering & Technology 7, no. 2.21 (2018): 206. http://dx.doi.org/10.14419/ijet.v7i2.21.12173.

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Change detection (CD) is the process of detecting changes from multitemporal satellite images that have undergone spatial changes due to natural and man-made disaster. The objective is to analyse different change detection techniques, in order to use appropriately in various applications with the help of image processing. Techniques that are used in current researches are Image Differencing, Image Regression, Change Vector Analysis (CVA),Principal Component Analysis(PCA), Tasselled Cap, Gramm-Schmidt(GS), Post Classification Comparison, EM Detection, Unsupervised Change Detection, Li-Strahler

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Jung, Sejung, Won Hee Lee, and Youkyung Han. "Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles." Remote Sensing 13, no. 18 (2021): 3660. http://dx.doi.org/10.3390/rs13183660.

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Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building

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Lu, D., P. Mausel, E. Brondízio, and E. Moran. "Change detection techniques." International Journal of Remote Sensing 25, no. 12 (2004): 2365–401. http://dx.doi.org/10.1080/0143116031000139863.

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Bose, Aniruddha, and Kunal Ray. "Fast Change Detection." Defence Science Journal 61, no. 1 (2011): 51–56. http://dx.doi.org/10.14429/dsj.61.479.

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Michel, Ulrich, and Manfred Ehlers. "Editoral ,,Change Detection“." Photogrammetrie - Fernerkundung - Geoinformation 2011, no. 4 (2011): 203–4. http://dx.doi.org/10.1127/1432-8364/2011/0082.

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Menzel, Susanne, Thomas Hummel, Laura Schäfer, Cornelia Hummel, and Ilona Croy. "Olfactory change detection." Biological Psychology 140 (January 2019): 75–80. http://dx.doi.org/10.1016/j.biopsycho.2018.11.010.

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Alien, M. R., C. T. Mutlow, G. M. C. Blumberg, J. R. Christy, R. T. McNider, and D. T. Llewellyn-Jones. "Global change detection." Nature 370, no. 6484 (1994): 24. http://dx.doi.org/10.1038/370024b0.

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Ohlsson, Henrik, Tianshi Chen, Sina Khoshfetrat Pakazad, Lennart Ljung, and S. Shankar Sastry. "Distributed Change Detection*." IFAC Proceedings Volumes 45, no. 16 (2012): 77–82. http://dx.doi.org/10.3182/20120711-3-be-2027.00409.

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Wang, Jiangqing, Juanjuan Tian, Lu Zheng, et al. "MT-SiamNet: A Multi-Scale Attention Network for Reducing Missed Detections in Farmland Change Detection." Applied Sciences 15, no. 6 (2025): 3061. https://doi.org/10.3390/app15063061.

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Farmland changes have a profound impact on agricultural ecosystems and global food security, making the timely and accurate detection of these changes crucial. Remote sensing image change detection provides an effective tool for monitoring farmland dynamics, but existing methods often struggle with high-resolution images due to complex scenes and insufficient multi-scale information capture, particularly in terms of missed detections. Missed detections can lead to underestimating land changes, which affects key areas such as resource allocation, agricultural decision-making, and environmental

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Angelone, Bonnie L., Daniel T. Levin, and Daniel J. Simons. "The Relationship between Change Detection and Recognition of Centrally Attended Objects in Motion Pictures." Perception 32, no. 8 (2003): 947–62. http://dx.doi.org/10.1068/p5079.

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Observers typically detect changes to central objects more readily than changes to marginal objects, but they sometimes miss changes to central, attended objects as well. However, even if observers do not report such changes, they may be able to recognize the changed object. In three experiments we explored change detection and recognition memory for several types of changes to central objects in motion pictures. Observers who failed to detect a change still performed at above chance levels on a recognition task in almost all conditions. In addition, observers who detected the change were no m

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Huang, Zhongxin, Xiaomei Yang, Yueming Liu, et al. "Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model." Remote Sensing 17, no. 5 (2025): 787. https://doi.org/10.3390/rs17050787.

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Change detection of cultivated land parcels is critical for achieving refined management of farmland. However, existing change detection methods based on high-resolution remote sensing imagery focus primarily on cultivation type changes, neglecting the importance of detecting parcel pattern changes. To address the issue of detecting diverse types of changes in cultivated land parcels, this study constructs an automated workflow framework for change detection, based on the unsupervised segmentation method of the SAM (Segment Anything Model). By performing spatial connection analysis on cultivat

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Yadav, R., A. Nascetti, and Y. Ban. "BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1377–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022.

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Abstract. Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automat

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Rodway, Paul, Karen Gillies, and Astrid Schepman. "Vivid Imagers Are Better at Detecting Salient Changes." Journal of Individual Differences 27, no. 4 (2006): 218–28. http://dx.doi.org/10.1027/1614-0001.27.4.218.

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This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture'

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22

Yang, Mingzhe, Yuan Zhou, Yanjie Feng, and Shuwei Huo. "Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images." Applied Sciences 14, no. 13 (2024): 5415. http://dx.doi.org/10.3390/app14135415.

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Building change detection monitors building changes by comparing and analyzing multi-temporal images acquired from the same area and plays an important role in land resource planning, smart city construction and natural disaster assessment. Different from change detection in conventional scenes, buildings in the building change detection task usually appear in a densely distributed state, which is easy to be occluded; at the same time, building change detection is easily interfered with by shadows generated by light and similar-colored features around the buildings, which makes the edges of th

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Kedgley, Mark. "Change detection technology has changed – for the better." Computer Fraud & Security 2014, no. 7 (2014): 8–10. http://dx.doi.org/10.1016/s1361-3723(14)70511-1.

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Haigang, Sui, Li Deren, Gong Jianya, and Zhu Qing. "Analysis and representation of changes in change detection." Geo-spatial Information Science 5, no. 2 (2002): 13–16. http://dx.doi.org/10.1007/bf02833880.

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Santoso, Heri, Abdul Halim Hasugian, and Yusuf Ramadhan Nasution. "Aplikasi Deteksi Perubahan Wilayah dengan Menggunakan Metode Post-Classification." JURNAL ARMADA INFORMATIKA 3, no. 1 (2019): 90–104. http://dx.doi.org/10.36520/jai.v3i1.48.

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Changes that occur in the region is one of the problems that are considered significant and strategic that occur in each region specifically in the region ofb. one of the important issues for planners and decision makers in urban and regional policies. Data, information, and tools sometimes turn into a burden in the process of detecting changes in land use. Along with advances in technology to detect changes in an area that are usually done manually (visible, ordinary photos), now it has begun to shift to the use of image technology (satellite), where this is caused by satellite technology, en

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Ahangarha, Marjan, Reza Shah-Hosseini, and Mohammad Saadatseresht. "Deep Learning-Based Change Detection Method for Environmental Change Monitoring Using Sentinel-2 Datasets." Environmental Sciences Proceedings 5, no. 1 (2020): 15. http://dx.doi.org/10.3390/iecg2020-08544.

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Change detection (CD) is an essential tool for the accurate understanding of land surface changes using Earth observation data and is extremely important for detecting the interactions between social and natural occurrences in geoscience. Binary change detection aims to detect changes and no changing areas, since improving the quality of the binary CD map is an important issue in remote sensing images; in this paper, a supervised deep learning (DL)-based change detection method was proposed to generate an accurate change map. Due to the good performance and great potential of DL in the domain

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Mulahusić, Admir, and Nedim Tuno. "Methods for Change Detection in Remote Sensing." Geodetski glasnik, no. 40 (March 31, 2011): 3–13. http://dx.doi.org/10.58817/2233-1786.2011.45.40.3.

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In this paper, the different ways to identify changes in remote sensing are given. Various authors have presented different methods of detecting changes on the Earth's surface. Detection of changes, among other things, are very important for tracking changes, as well as assessment and evaluation of changes and interrelations of natural and artificial objects. All this leads to better understanding of potential causes of change.

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Sun, Z., F. Duan, H. Guan, F. Yang, Y. Wang, and W. Zhao. "A FULLY CONNECTED CHANGE DETECTION METHOD OF SAR IMAGES FUSING ORIGINAL IMAGE FEATURES AND CHANGE DETECTION RESULTS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 1271–80. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1271-2023.

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Abstract. The primary strategy to eliminate the effect of scatter noise in synthetic aperture radar (SAR) imagery is usually through filtering or combining neighborhood information. However, both approaches to reducing noise reduce the detection accuracy of change edges with similar characteristics to scatter noise points. Considering the above problems, this letter proposes a post-processing method that applies a fully connected conditional random field theoretical model to fuse the original image information with the initial change detection results. The method first takes the original image

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Tan, M., and M. Hao. "CHANGE DETECTION BY FUSING ADVANTAGES OF THRESHOLD AND CLUSTERING METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 897–901. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-897-2017.

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In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximum (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogeneous CD map but with some missed detections. Therefore, we aim to design a framework to improve CD results by fusing the advantages of threshold and clustering methods. Experimental results indicate the effecti

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Shi, Wenzhong, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. "Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges." Remote Sensing 12, no. 10 (2020): 1688. http://dx.doi.org/10.3390/rs12101688.

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Change detection based on remote sensing (RS) data is an important method of detecting changes on the Earth’s surface and has a wide range of applications in urban planning, environmental monitoring, agriculture investigation, disaster assessment, and map revision. In recent years, integrated artificial intelligence (AI) technology has become a research focus in developing new change detection methods. Although some researchers claim that AI-based change detection approaches outperform traditional change detection approaches, it is not immediately obvious how and to what extent AI can improve

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Brönnimann, Stefan. "Climate Change: Detection and impacts." Prace Geograficzne, no. 175 (December 30, 2024): 9. https://doi.org/10.4467/20833113pg.24.009.2095.

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Although global warming has been ongoing for decades, monitoring and detecting climate change remain important task, as evidenced by the abrupt warming of 2023/2024 that caught even scientists by surprise. When will we reach 1.5 °C of global warming above pre- -industrial levels? How unusual is the current temperature from a long-term perspective, and how unusual are current climate extremes? This paper summarizes the challenge of climate change detection over the past 50 years as well as the past 300 years. The paper addresses recent global trends in thermodynamic quantities, as well as longe

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Shen, Yuzhen, Yuchun Wei, Hong Zhang, Xudong Rui, Bingbing Li, and Junshu Wang. "Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu." Remote Sensing 16, no. 8 (2024): 1357. http://dx.doi.org/10.3390/rs16081357.

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Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of

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Bhinder Devaraj Verma, Dilraj. "Literature Review on Change Detection Using Remote Sensing Imagery." International Journal of Science and Research (IJSR) 12, no. 2 (2023): 1515–23. http://dx.doi.org/10.21275/sr23218134434.

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Suzuki, Ikumi, Kazuo Hara, and Eiji Murakami. "Hubness Change Point Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 12622–30. https://doi.org/10.1609/aaai.v39i12.33376.

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This study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search. Therefore, many studies in the past have focused on reducing hubness to improve accuracy. In contrast, this study utilizes hubness to detect changes. Specifically, if there is no change, suppressing the hubness occurring in the two datasets obtained by dividing the time series data

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Liu, Qunqun, Shiquan Wan, and Bin Gu. "A Review of the Detection Methods for Climate Regime Shifts." Discrete Dynamics in Nature and Society 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/3536183.

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An abrupt climate change means that the climate system shifts from a steady state to another steady state. Study on the phenomenon and theory of the abrupt climate change is a new research field of modern climatology, and it is of great significance for the prediction of future climate change. The climate regime shift is one of the most common forms of abrupt climate change, which mainly refers to the statistical significant changes on the variable of climate system at one time scale. These detection methods can be roughly divided into five categories based on different types of abrupt changes

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Zhu, Ling, Dejun Gao, Tao Jia, and Jingyi Zhang. "Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes." Remote Sensing 13, no. 16 (2021): 3244. http://dx.doi.org/10.3390/rs13163244.

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To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco

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Park, Hyunwoo, Seung Jun Shin, and Gyun-Soo Yoon. "Analysis of Temperature Changes in Korea via Change Point Detection Algorithm." Korean Data Analysis Society 26, no. 6 (2024): 1777–87. https://doi.org/10.37727/jkdas.2024.26.6.1777.

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Global warming is increasingly recognized as a pressing global issue, with South Korea experiencing a rate of temperature increase exceeding the global average. The impacts of these climatic changes span various sectors, emphasizing the need for a deeper understanding of temperature trends and their implications. This study investigates long-term temperature trends in South Korea by applying popular change point detection (CPD) algorithms to identify when significant temperature changes occurred. Using daily average temperature data from eight major cities from 1969-2023, we employ CPD methods

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Huang, Rui, Ruofei Wang, Yuxiang Zhang, Yan Xing, Wei Fan, and Kai Leung Yung. "Selecting change image for efficient change detection." IET Signal Processing 16, no. 3 (2021): 327–39. http://dx.doi.org/10.1049/sil2.12095.

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Wilken, Patrick, and Wei Ji Ma. "A detection theory account of change detection." Journal of Vision 4, no. 12 (2004): 11. http://dx.doi.org/10.1167/4.12.11.

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Cheng, Guangliang, Yunmeng Huang, Xiangtai Li, et al. "Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review." Remote Sensing 16, no. 13 (2024): 2355. http://dx.doi.org/10.3390/rs16132355.

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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 c

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Bashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.

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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recog

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Hayashi, Shogo, Yoshinobu Kawahara, and Hisashi Kashima. "Active Change-Point Detection." Transactions of the Japanese Society for Artificial Intelligence 35, no. 5 (2020): E—JA10_1–10. http://dx.doi.org/10.1527/tjsai.35-5_e-ja10.

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Veeravalli, V. V. "Decentralized quickest change detection." IEEE Transactions on Information Theory 47, no. 4 (2001): 1657–65. http://dx.doi.org/10.1109/18.923755.

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Listner, Clemens, and Irmgard Niemeyer. "Object-based Change Detection." Photogrammetrie - Fernerkundung - Geoinformation 2011, no. 4 (2011): 233–45. http://dx.doi.org/10.1127/1432-8364/2011/0085.

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Toyofuku, N., T. E. Cohn, and T. Nguyen. "Transient size change detection." Journal of Vision 2, no. 7 (2010): 676. http://dx.doi.org/10.1167/2.7.676.

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Olds, E. S., and M. D. Degani. "Change detection and heterogeneity." Journal of Vision 3, no. 9 (2010): 333. http://dx.doi.org/10.1167/3.9.333.

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Nasri, Masoud, and Reza Modarres. "Hydrologic Drought Change Detection." Natural Hazards Review 20, no. 1 (2019): 04018022. http://dx.doi.org/10.1061/(asce)nh.1527-6996.0000301.

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Kenemans, J. Leon, Tineke Grent-'t Jong, and Marinus N. Verbaten. "Detection of visual change." NeuroReport 14, no. 9 (2003): 1239–42. http://dx.doi.org/10.1097/00001756-200307010-00010.

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Moustakides, George V. "Sequential change detection revisited." Annals of Statistics 36, no. 2 (2008): 787–807. http://dx.doi.org/10.1214/009053607000000938.

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