Relevant bibliographies by topics / Change detection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Change detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Change detection"

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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 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 (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 (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 (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 (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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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 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 (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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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 bridging layers, respectively. Finally, we extend our approach to video, leveraging rich temporal information to enhance the performance of scene change detection. We compare our approach and baseline through various experiments. While existing train-based baseline tend to specialize only in the trained domain, our method shows consistent performance across various domains, proving the competitiveness of our approach.
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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 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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Dissertations / Theses on the topic "Change detection"

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Wang, Bo. "Structural change detection via penalized regression." Diss., University of Iowa, 2018. https://ir.uiowa.edu/etd/6520.

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This dissertation research addresses how to detect structural changes in stochastic linear models. By introducing a special structure to the design matrix, we convert the structural change detection problem to a variable selection problem. There are many existing variable selection strategies, however, they do not fully cope with structural change detection. We design two penalized regression algorithms specifically for the structural change detection purpose. We also propose two methods involving these two algorithms to accomplish a bi-level structural change detection: they locate the change points and also recognize which predictors contribute to the variation of the model structure. Extensive simulation studies are shown to demonstrate the effectiveness of the proposed methods in a variety of settings. Furthermore, we establish asymptotic theoretical properties to justify the bi-level detection consistency for one of the proposed methods. In addition, we write an R package with computationally efficient algorithms for detecting structural changes. Comparing to traditional methods, the proposed algorithms showcase enhanced detection power and more estimation precision, with added capacity of specifying the model structures at all regimes.
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Hofer, Heiko. "Large-Scale Gradual Change Detection." Neubiberg Universitätsbibliothek der Universität der Bundeswehr, 2010. http://d-nb.info/1001920856/34.

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Yang, Jiangbin. "Change detection in autocorrelated processes." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0009/NQ41349.pdf.

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Comber, Alexis. "Automated land cover change detection." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248628.

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This thesis describes a generic approach for automated land cover monitoring. Knowledge about land cover is acquired through a knowledge acquisition exercise and used to augment image analysis in order to determine land cover change direction. It is demonstrated that taking a task-oriented approach to the change problem avoids the specificity of more traditional data-oriented approaches. The approach described here involves four key analyses that have contributed to the overall problem solution: Identifying the knowledge used to determine different land cover elements (elicitation and modelling); Land cover remote sensing characteristics; Land cover bio-geographic characteristics; Investigation of the most suitable approach for combining evidence. The results of these investigations were applied to semi-natural change problems: evidence about areas known to have changed was reasoned with and change directions identified by applying the knowledge and interpreter rules of thumb. The results were compared with field surveys of the change areas, and were shown to have correctly identified the land cover change direction in each case.
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Bashir, Sulaimon A. "Change detection for activity recognition." Thesis, Robert Gordon University, 2017. http://hdl.handle.net/10059/3104.

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Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blind. They continuously adapt a classification model at a regular interval without specific and precise detection of the indicator of the degrading performance of the model. This approach can lead to wastage of system resources dedicated to continuous adaptation. This thesis addresses the problem of detecting changes in the accuracy of activity recognition model. The thesis developed a classifier for activity recognition. The classifier uses three statistical summaries data that can be generated from any dataset for similarity based classification of new samples. The weighted ensemble combination of the classification decision from each statistical summary data results in a better performance than three existing benchmarked classification algorithms. The thesis also presents change detection approaches that can detect the changes in the accuracy of the underlying recognition model without having access to the ground truth label of each activity being recognised. The first approach called `UDetect' computes the change statistics from the window of classified data and employed statistical process control method to detect variations between the classified data and the reference data of a class. Evaluation of the approach indicates a consistent detection that correlates with the error rate of the model. The second approach is a distance based change detection technique that relies on the developed statistical summaries data for comparing new classified samples and detects any drift in the original class of the activity. The implemented approach uses distance function and a threshold parameter to detect the accuracy change in the classifier that is classifying new instances. Evaluation of the approach yields above 90% detection accuracy. Finally, a layered framework for activity recognition is proposed to make model adaptation in activity recognition informed using the developed techniques in this thesis.
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Jones, Zygmunt. "Wide-baseline image change detection." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/32406.

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Growth in the prevalence of cameras has resulted in larger amounts of available image data. This has resulted in demand for automated methods of analysing this data. One key area of demand is automated change detection, the automated detection of changes in a scene, as recorded by a reference and sample image. Established methods of change detection tend to rely on the reference and sample image being captured from the same position, but much of the available data does not fit this criteria. This thesis presents novel approaches to key challenges in wide-baseline cases involving differences in viewing angle of up to 30 degrees, including registration and the image region matching that are robust to the inherent registration errors. The developed algorithms are then combined into an end-to-end system. This thesis presents novel registration approaches including the use of a Delaunay triangulation mask that enables registration of each component triangle, a method of finding local planes in scenes by clustering matched feature points, the use of edge detection to register the edges of objects, and a method for registering planes that are orthogonal to a defined image plane and to the camera line. These techniques allow for the registration of complex 3D scenes with viewing angles of up to 30 degrees. The density of the available correspondences obtained using feature points is a key limiting factor in these methods and so ASIFT, a extension to the SIFT feature point that improves performance at wide angles is also introduced. ASIFT is shown to have an order of magnitude increase in correctly matches feature point density at 30 degrees. Though robust to wide differences in viewing angle, these registration techniques do nonetheless introduce registration errors of up to a few dozen pixels. For this reason the dense SIFT and shifted dense SIFT image comparison algorithms which are robust to registration errors of a few dozen pixels are also developed. The development of these comparison methods includes an analysis of SIFT descriptor statistics and their correlation. Finally these techniques are combined to form an end-to-end change detection system which is evaluated on a number of test datasets.
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Almutairi, Abdullah. "Monitoring land-cover change detection in an arid urban environment a comparison of change detection techniques /." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1410.

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Thesis (M.A.)--West Virginia University, 2000.<br>Title from document title page. Document formatted into pages; contains xi, 77 p. : ill. (some col.), maps (some col.) Includes abstract. Includes bibliographical references (p. 74-77).
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Vongsy, Karmon Marie. "CHANGE DETECTION METHODS FOR HYPERSPECTRAL IMAGERY." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1184010751.

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Yousif, Osama. "Change Detection Using Multitemporal SAR Images." Licentiate thesis, KTH, Geodesi och geoinformatik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-123494.

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Multitemporal SAR images have been used successfully for the detection of different types of environmental changes. The detection of urban change using SAR images is complicated due to the special characteristics of SAR images—for example, the existence of speckle and the complex mixture of the urban environment. This thesis investigates the detection of urban changes using SAR images with the following specific objectives: (1) to investigate unsupervised change detection, (2) to investigate reduction of the speckle effect and (3) to investigate spatio-contextual change detection. Beijing and Shanghai, the largest cities in China, were selected as study areas. Multitemporal SAR images acquired by ERS-2 SAR (1998~1999) and Envisat ASAR (2008~2009) sensors were used to detect changes that have occurred in these cities. Unsupervised change detection using SAR images is investigated using the Kittler-Illingworth algorithm. The problem associated with the diversity of urban changes—namely, more than one typology of change—is addressed using the modified ratio operator. This operator clusters both positive and negative changes on one side of the change-image histogram. To model the statistics of the changed and the unchanged classes, four different probability density functions were tested. The analysis indicates that the quality of the resulting change map will strongly depends on the density model chosen. The analysis also suggests that use of a local adaptive filter (e.g., enhanced Lee) removes fine geometric details from the scene. Speckle suppression and geometric detail preservation in SAR-based change detection, are addressed using the nonlocal means (NLM) algorithm. In this algorithm, denoising is achieved through a weighted averaging process, in which the weights are a function of the similarity of small image patches defined around each pixel in the image. To decrease the computational complexity, the PCA technique is used to reduce the dimensionality of the neighbourhood feature vectors. Simple methods to estimate the dimensionality of the new space and the required noise variance are proposed. The experimental results show that the NLM algorithm outperformed traditional local adaptive filters (e.g., enhanced Lee) in eliminating the effect of speckle and in maintaining the geometric structures in the scene. The analysis also indicates that filtering the change variable instead of the individual SAR images is effective in terms of both the quality of the results and the time needed to carry out the computation. The third research focuses on the application of Markov random field (MRF) in change detection using SAR images. The MRF-based change detection algorithm shows limited capacity to simultaneously maintain fine geometric detail in urban areas and combat the effect of speckle noise. This problem has been addressed through the introduction of a global constraint on the pixels’ class labels. Based on NLM theory, a global probability model is developed. The iterated conditional mode (ICM) scheme for the optimization of the MAP-MRF criterion function is extended to include a step that forces the maximization of the global probability model. The experimental results show that the proposed algorithm is better at preserving the fine structural detail, effective in reducing the effect of speckle, less sensitive to the value of the contextual parameter, and less affected by the quality of the initial change map compared with traditional MRF-based change detection algorithm.<br><p>QC 20130610</p>
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Brolin, Morgan. "Automatic Change Detection in Visual Scenes." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301611.

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This thesis proposes a Visual Scene Change Detector(VSCD) system which is a system which involves four parts, image retrieval, image registration, image change detection and panorama creation. Two prestudies are conducted in order to find a proposed image registration method and a image retrieval method. The two found methods are then combined with a proposed image registration method and a proposed panorama creation method to form the proposed VSCD. The image retrieval prestudy evaluates a SIFT related method with a bag of words related method and finds the SIFT related method to be the superior method. The image change detection prestudy evaluates 8 different image change detection methods. Result from the image change detection prestudy shows that the methods performance is dependent on the image category and an ensemble method is the least dependent on the category of images. An ensemble method is found to be the best performing method followed by a range filter method and then a Convolutional Neural Network (CNN) method. Using a combination of the 2 image retrieval methods and the 8 image change detection method 16 different VSCD are formed and tested. The final result show that the VSCD comprised of the best methods from the prestudies is the best performing method.<br>Detta exjobb föreslår ett Visual Scene Change Detector(VSCD) system vilket är ett system som har 4 delar, image retrieval, image registration, image change detection och panorama creation. Två förstudier görs för att hitta en föreslagen image registration metod och en föreslagen panorama creation metod. De två föreslagna delarna kombineras med en föreslagen image registration och en föreslagen panorama creation metod för att utgöra det föreslagna VSCD systemet. Image retrieval förstudien evaluerar en ScaleInvariant Feature Transform (SIFT) relaterad method med en Bag of Words (BoW) relaterad metod och hittar att den SIFT relaterade methoden är bäst. Image change detection förstudie visar att metodernas prestanda är beroende av catagorin av bilder och att en enemble metod är minst beroende av categorin av bilder. Enemble metoden är hittad att vara den bästa presterande metoden följt av en range filter metod och sedan av en CNN metod. Genom att använda de 2 image retrieval metoder kombinerat med de 8 image change detection metoder är 16 st VSCD system skapade och testade. Sista resultatet visar att den VSCD som använder de bästa metoderna från förstudien är den bäst presterande VSCD.
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Books on the topic "Change detection"

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Sampson, Philip A. Change detection. UMIST, 1998.

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Polich, John, ed. Detection of Change. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0294-4.

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İlsever, Murat, and Cem Ünsalan. Two-Dimensional Change Detection Methods. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4255-3.

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Gustafsson, Fredrik. Adaptive Filtering and Change Detection. John Wiley & Sons, Ltd, 2001. http://dx.doi.org/10.1002/0470841613.

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Kurt, Mehmet Necip. Data-Driven Quickest Change Detection. [publisher not identified], 2020.

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Gustafsson, Fredrik. Adaptive filtering and change detection. Wiley, 2000.

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World Meteorological Organization. Commission for Climatology. and World Climate Data and Monitoring Programme., eds. Climate change detection report: Reports for CCL-XII from rapporteurs that relate to climate change detection. World Meteorological Organization, 1997.

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Jenssen, A. C. Algorithms for change detection and diagnosis indynamicplants. UMIST, 1994.

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Baruch, Menahem. Mass change detection based on reduced measurements. Technion-Israel Institute of Technology, Faculty of Aerospace Engineering, 1995.

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Behrens, Richard J. Change detection analysis with spectral thermal imagery. Naval Postgraduate School, 1998.

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Book chapters on the topic "Change detection"

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_129-2.

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Théau, Jérôme. "Change Detection." In Springer Handbook of Geographic Information. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-72680-7_7.

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-17885-1_129.

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Théau, Jérôme. "Change Detection." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_129.

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Tenneson, Karis, John Dilger, Crystal Wespestad, et al. "Change Detection." In Cloud-Based Remote Sensing with Google Earth Engine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26588-4_16.

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AbstractThis chapter introduces change detection mapping. It will teach you how to make a two-date land cover change map using image differencing and threshold-based classification. You will use what you have learned so far in this book to produce a map highlighting changes in the land cover between two time steps. You will first explore differences between the two images extracted from these time steps by creating a difference layer. You will then learn how to directly classify change based on the information in both of your images.
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Mundy, Joseph L. "Change Detection." In Computer Vision. Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_214.

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Wright, Anthony A. "Change Detection." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47829-6_1590-1.

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Canty, Morton John. "Change Detection." In Image Analysis, Classification, and Change Detection in Remote Sensing. CRC Press, 2019. http://dx.doi.org/10.1201/9780429464348-9.

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Wright, Anthony A. "Change Detection." In Encyclopedia of Animal Cognition and Behavior. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-55065-7_1590.

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Théau, Jérôme. "Change Detection." In Springer Handbook of Geographic Information. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-53125-6_7.

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Conference papers on the topic "Change detection"

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Chen, Yu-Zhen Janice, Jinhang Zuo, Venugopal V. Veeravalli, and Don Towsley. "Quickest Change Detection with Confusing Change." In 2024 58th Asilomar Conference on Signals, Systems, and Computers. IEEE, 2024. https://doi.org/10.1109/ieeeconf60004.2024.10942912.

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A, Sujith Kumar, Venkatesan Meenakshi Sundaram, and P. Prabhavathy. "Change Vector Change Indices Method for Unsupervised Remote Sensing Change Detection." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627810.

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Noman, Mubahsir, Mustansar Fiaz, and Hisham Cholakkal. "ChangeBind: A Hybrid Change Encoder for Remote Sensing Change Detection." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10640559.

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Wu, Jiawen. "Residual Attention-Enhanced Bitemporal Change Detection with Pseudo-Change Suppression." In 2025 4th International Symposium on Computer Applications and Information Technology (ISCAIT). IEEE, 2025. https://doi.org/10.1109/iscait64916.2025.11010307.

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Xie, Tao, Lei Fu, Dong Zhao, Yang Hu, Hantong Xing, and Shuang Wang. "Object-Level Change Detection via Siamese Detection Network." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641859.

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Mitkari, Kavita V., Manoj K. Arora, and Reet K. Tiwari. "Detecting Glacier Surface Changes Using Object-Based Change Detection." In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2018. http://dx.doi.org/10.1109/igarss.2018.8519230.

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Suzuki, Tomoyuki, Munetaka Minoguchi, Ryota Suzuki, et al. "Semantic Change Detection." In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2018. http://dx.doi.org/10.1109/icarcv.2018.8581264.

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de Carvalho, Osmar Abilio, Renato Fontes, and Nilton Correia da Silva. "Spectral change detection." In 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4423205.

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Buades, A., J. L. Lisani, and L. Rudin. "Adaptive Change Detection." In 2009 16th International Conference on Systems, Signals and Image Processing. IEEE, 2009. http://dx.doi.org/10.1109/iwssip.2009.5367788.

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Borgolte, Kevin, Christopher Kruegel, and Giovanni Vigna. "Relevant change detection." In the 23rd International Conference. ACM Press, 2014. http://dx.doi.org/10.1145/2567948.2578039.

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Reports on the topic "Change detection"

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McCulloh, Ian A., and Kathleen M. Carley. Social Network Change Detection. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487504.

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McCulloh, Ian, Matthew Webb, John Graham, Kathleen Carley, and Daniel B. Horn. Change Detection in Social Networks. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada484175.

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Tillman, Stan, ed. OWS-7 Motion Video Change Detection. Open Geospatial Consortium, Inc., 2010. http://dx.doi.org/10.62973/10-036r2.

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Lohrenz, M. C., M. L. Gendron, and G. J. Layne. Automic Change Detection and Classification (ACDC) System. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada494240.

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Bickel, Douglas L. On Radar Resolution in Coherent Change Detection. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1227341.

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Liu, F., and L. A. Bush. Activity Level Change Detection for Persistent Surveillance. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada457106.

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Miao, B. Q., and L. C. Zhao. Detection of Change Points Using Rank Methods. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada198406.

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Wigley, T. M. L., and P. D. Jones. Detection of greenhouse-gas-induced climatic change. Office of Scientific and Technical Information (OSTI), 1992. http://dx.doi.org/10.2172/7015088.

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Jones, P. D., and T. M. L. Wigley. Detection of Greenhouse-Gas-Induced Climatic Change. Office of Scientific and Technical Information (OSTI), 1998. http://dx.doi.org/10.2172/6615.

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G-Michael, Tesfaye, Bradley Marchand, J. D. Tucker, Daniel D. Sternlicht, Timothy M. Marston, and Mahmood R. Azimi-Sadjadi. Automated Change Detection for Synthetic Aperture Sonar. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada601363.

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