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Journal articles on the topic 'Change detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 July 2024

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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 (August 26, 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 dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

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Park, H. B., and J. S. Hyun. "Detecting a pop-out visual change can impair subsequent detection of another change in change detection." Journal of Vision 13, no. 9 (July 25, 2013): 322. http://dx.doi.org/10.1167/13.9.322.

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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 (September 11, 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 method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.

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Kennette, Lynne N., Lee H. Wurm, and Lisa R. Van Havermaet. "Change detection." Mental Lexicon 5, no. 1 (June 18, 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, focused changes were detected more often, as were changes to unrelated words. Proficiency was related to change detection for monolinguals and bilinguals. The relationships between these and other participant and stimulus variables are also explored.

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Rensink, Ronald A. "Change Detection." Annual Review of Psychology 53, no. 1 (February 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 detecting changes manually is very time consuming, the aim of this study is to provide reliable change detections for different building sizes in order to support NMAs in their task to update their databases. As datasets of different times may have varying point densities due to technological advancements or different sensors, we propose a raster-based approach, which is independent of the point density altogether. Within a raster cell, our approach considers the height distribution of all points for two points in time by exploiting the Jensen-Shannon distance to measure their similarity. Our proposed method outperforms simple threshold methods on detecting building changes with respect to the same or different point cloud types. In combination with our proposed class change detection approach, we achieve a change detection performance measured by the mean F1-Score of about 71% between two ALS and about 60% between ALS and DIM point clouds acquired at different times.

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

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8

Bose, Aniruddha, and Kunal Ray. "Fast Change Detection." Defence Science Journal 61, no. 1 (January 6, 2011): 51–56. http://dx.doi.org/10.14429/dsj.61.479.

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9

Michel, Ulrich, and Manfred Ehlers. "Editoral ,,Change Detection“." Photogrammetrie - Fernerkundung - Geoinformation 2011, no. 4 (August 1, 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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11

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 (July 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 (July 2012): 77–82. http://dx.doi.org/10.3182/20120711-3-be-2027.00409.

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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 (March 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 to a task-irrelevant peripheral cue. Under conditions of visual fixation, change detection was above chance across the entire visual area tested. In addition, a “hot spot” of attention (corresponding to near-perfect change detection) elongated along the cue-fixation axis, such that performance improved not only at the cued location but also in the opposite hemifield.

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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 (September 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 students were assigned to one of two cueing conditions (reliable or neutral) and watched twenty videos. Ten of the videos were interrupted by having participants turn their heads to attend to a secondary display while an object changed in the video on the primary monitor. Eight videos (half with and without interruptions) had a single object that underwent a perceptual feature change in color, brightness, appearance, or disappearance. Overall, participants were very poor at detecting target changes. However, participants detected more object changes during uninterrupted versus interrupted trials. Providing a cue related to the object change did not improve detection performance. Overall, these results support other dynamic change detection findings that show the difficulty of detecting targets within dynamic environments even when provided reliable information.

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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 (September 13, 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 detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods.

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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 automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.

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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 (April 20, 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 Reflectance Model, Spectral Mixture Model, Biophysical Parameter Method, Integrated GIS and Remote Sensing Method, GIS Approach, Visual Interpretation and so on. Effective change detection is required for various applications such as rate of deforestation, costal changes, urban developments, damage evaluation, resource monitoring and land disposition.

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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 (August 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 more accurate in their recognition than those who did not detect the change. Despite large differences in the detectability of changes across conditions, those observers who missed the change did not vary in their ability to recognize the changing object.

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Kedgley, Mark. "Change detection technology has changed – for the better." Computer Fraud & Security 2014, no. 7 (July 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 (January 2002): 13–16. http://dx.doi.org/10.1007/bf02833880.

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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 (February 5, 2023): 1515–23. http://dx.doi.org/10.21275/sr23218134434.

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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 information and the initial change detection results as a priori conditions. Secondly, the global spatial information in the original image and the label values in the initial change detection results are fully considered when detecting the changed and unchanged pixels to establish a fully connected relationship between all the pixels and find the label distribution probability of each pixel under the condition of noise suppression, and finally obtain better change detection results. The experimental results on the real SAR dataset confirm the proposed method's effectiveness, robustness, and efficiency.

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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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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 effectiveness of the proposed method.

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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 (January 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's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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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 (November 25, 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 of pattern recognition and nonlinear problem modeling, DL is becoming popular to resolve the CD problem using multitemporal remote sensing imageries. The purpose of using DL algorithms and especially convolutional neural networks (CNN) is to monitor the environmental change into change and no change classes. The Onera Satellite Change Detection (OSCD) datasets were used to evaluate the proposed method. Experimental results on the real dataset showed the effectiveness of the proposed algorithm. The overall accuracy and the kappa coefficient of the change map using the proposed method is over 95% and close to one, respectively.

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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 (December 24, 2021): 327–39. http://dx.doi.org/10.1049/sil2.12095.

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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 (June 21, 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 the changed region challenging to be distinguished. Aiming at the above problems, this paper utilizes edge information to guide the neural network to learn edge features related to changes and suppress edge features unrelated to changes, so as to accurately extract building change information. First, an edge-extracted module is designed, which combines deep and shallow features to supplement the lack of feature information at different resolutions and to extract the edge structure of the changed features; second, an edge-guided module is designed to fuse the edge features with different levels of features and to guide the neural network to focus on the confusing building edge regions by increasing the edge weights to improve the network’s ability to detect the edges that have changed. The proposed building change detection algorithm has been validated on two publicly available data (WHU and LEVIR-CD building change detection datasets). The experimental results show that the proposed model achieves 91.14% and 89.76% in F1 scores, respectively, demonstrating superior performance compared to some recent learning change detection methods.

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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 (August 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, enables the detection of regional changes to be carried out on a wide scale, the time required is more effective and effective compared to conventional regional change detection techniques. Change detection (change detection) is a process of identifying changes in the shape of the surface in a vegetation cover or as a spectral / spatial movement of vegetation bodies over time. Among the change detection methods is to use Post-Classification with consideration of the ease of implementation, where this method works by comparing 2 or more temporal images. Keywords: Detection of Regional Change, Post-Classification Method, application.

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Wilken, Patrick, and Wei Ji Ma. "A detection theory account of change detection." Journal of Vision 4, no. 12 (December 29, 2004): 11. http://dx.doi.org/10.1167/4.12.11.

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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 (September 1, 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 (May 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 (August 1, 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 (March 15, 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 (March 16, 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 (February 2019): 04018022. http://dx.doi.org/10.1061/(asce)nh.1527-6996.0000301.

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Suvorikova, A., and V. Spokoiny. "Multiscale Change Point Detection." Theory of Probability & Its Applications 61, no. 4 (January 2017): 665–91. http://dx.doi.org/10.1137/s0040585x97t988411.

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Kenemans, J. Leon, Tineke Grent-'t Jong, and Marinus N. Verbaten. "Detection of visual change." NeuroReport 14, no. 9 (July 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 (April 2008): 787–807. http://dx.doi.org/10.1214/009053607000000938.

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Alippi, Cesare, Giacomo Boracchi, and Manuel Roveri. "Hierarchical Change-Detection Tests." IEEE Transactions on Neural Networks and Learning Systems 28, no. 2 (February 2017): 246–58. http://dx.doi.org/10.1109/tnnls.2015.2512714.

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Eisinger, R., H. Y. Im, H. Pailian, and J. Halberda. "Ensemble-based Change Detection." Journal of Vision 13, no. 9 (July 25, 2013): 800. http://dx.doi.org/10.1167/13.9.800.

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Chen, Gang, Geoffrey J. Hay, Luis M. T. Carvalho, and Michael A. Wulder. "Object-based change detection." International Journal of Remote Sensing 33, no. 14 (January 23, 2012): 4434–57. http://dx.doi.org/10.1080/01431161.2011.648285.

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Dubey, Paromita, and Hans-Georg Müller. "Fréchet change-point detection." Annals of Statistics 48, no. 6 (December 2020): 3312–35. http://dx.doi.org/10.1214/19-aos1930.

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Ajmera, J., I. McCowan, and H. Bourlard. "Robust Speaker Change Detection." IEEE Signal Processing Letters 11, no. 8 (August 2004): 649–51. http://dx.doi.org/10.1109/lsp.2004.831666.

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Fliess, Michel, Cédric Join, and Mamadou Mboup. "Algebraic change-point detection." Applicable Algebra in Engineering, Communication and Computing 21, no. 2 (February 10, 2010): 131–43. http://dx.doi.org/10.1007/s00200-010-0119-z.

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Rosin, Paul L. "Thresholding for Change Detection." Computer Vision and Image Understanding 86, no. 2 (May 2002): 79–95. http://dx.doi.org/10.1006/cviu.2002.0960.

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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 (June 5, 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 recognition model without explicit detection of changes in the model performance. Design/methodology/approach The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.

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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, namely, abrupt mean value change, abrupt variance change, abrupt frequency change, abrupt probability density change, and the multivariable analysis. The main research progress of abrupt climate change detection methods is reviewed. What is more, some actual applications of those methods in observational data are provided. With the development of nonlinear science, many new methods have been presented for detecting an abrupt dynamic change in recent years, which is useful supplement for the abrupt change detection methods.

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Fan, Zhou, Ron O. Dror, Thomas J. Mildorf, Stefano Piana, and David E. Shaw. "Identifying localized changes in large systems: Change-point detection for biomolecular simulations." Proceedings of the National Academy of Sciences 112, no. 24 (May 29, 2015): 7454–59. http://dx.doi.org/10.1073/pnas.1415846112.

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Research on change-point detection, the classical problem of detecting abrupt changes in sequential data, has focused predominantly on datasets with a single observable. A growing number of time series datasets, however, involve many observables, often with the property that a given change typically affects only a few of the observables. We introduce a general statistical method that, given many noisy observables, detects points in time at which various subsets of the observables exhibit simultaneous changes in data distribution and explicitly identifies those subsets. Our work is motivated by the problem of identifying the nature and timing of biologically interesting conformational changes that occur during atomic-level simulations of biomolecules such as proteins. This problem has proved challenging both because each such conformational change might involve only a small region of the molecule and because these changes are often subtle relative to the ever-present background of faster structural fluctuations. We show that our method is effective in detecting biologically interesting conformational changes in molecular dynamics simulations of both folded and unfolded proteins, even in cases where these changes are difficult to detect using alternative techniques. This method may also facilitate the detection of change points in other types of sequential data involving large numbers of observables—a problem likely to become increasingly important as such data continue to proliferate in a variety of application domains.

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Xing, H., D. Hou, M. Lu, and J. Chen. "A LAND COVER CHANGE DETECTION METHOD BASED ON CHANGE DIFFERENCE MAP FUSION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 239–43. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-239-2019.

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<p><strong>Abstract.</strong> Direct radiometric comparison-based change detection methods have been widely used for detecting land cover change areas. However, the traditional methods are usually developed by using a single change index, which may cause omission or commission of land cover change. To address this challenge, we propose a change difference map fusion-based land cover change detection approach. First, operators of change vector analysis (CVA) and spectral gradient difference (SGD) are used for constructing change difference map, respectively. Second, image product weighted fusion method is introduced to construct a comprehensive change difference map. Finally, the expectation maximization algorithm and Bayesian rule with minimum error rate are applied to get the change/unchanged area. Experimental results of Landsat5 TM in 2010 and Landsat8 OLI in 2015 in two test areas are conducted. The experimental results show that accuracy of the proposed method is superior to the traditional change detection methods.</p>

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