Journal ArticleDOI
Image change detection algorithms: a systematic survey
TLDR
In this paper, the authors present a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling.Abstract:
Detecting regions of change in multiple images of the same scene taken at different times is of widespread interest due to a large number of applications in diverse disciplines, including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. This paper presents a systematic survey of the common processing steps and core decision rules in modern change detection algorithms, including significance and hypothesis testing, predictive models, the shading model, and background modeling. We also discuss important preprocessing methods, approaches to enforcing the consistency of the change mask, and principles for evaluating and comparing the performance of change detection algorithms. It is hoped that our classification of algorithms into a relatively small number of categories will provide useful guidance to the algorithm designer.read more
Citations
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Proceedings ArticleDOI
Background segmentation with feedback: The Pixel-Based Adaptive Segmenter
TL;DR: In this paper, a novel method for foreground segmentation is presented that follows a non-parametric background modeling paradigm, thus the background is modeled by a history of recently observed pixel values and the background update is based on a learning parameter.
Journal ArticleDOI
Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks
TL;DR: This paper presents a novel change detection approach for synthetic aperture radar images based on deep learning that accomplishes the detection of the changed and unchanged areas by designing a deep neural network.
Journal ArticleDOI
Change Detection in Synthetic Aperture Radar Images based on Image Fusion and Fuzzy Clustering
TL;DR: An unsupervised distribution-free change detection approach for synthetic aperture radar (SAR) images based on an image fusion strategy and a novel fuzzy clustering algorithm that exhibited lower error than its preexistences.
Journal ArticleDOI
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
Journal ArticleDOI
Object-based change detection
TL;DR: An overview of the main issues in change detection is presented, followed by the motivations for using OBCD as compared to pixel-based approaches, and a conceptual overview of solutions is provided.
References
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TL;DR: A review of recent as well as classic image registration methods to provide a comprehensive reference source for the researchers involved in image registration, regardless of particular application areas.