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
Classification based land use/land cover change detection through Landsat images
TL;DR: This paper attempts to contribute in two ways classification of remotely sensed images to different classes and time sequence analysis of satellite images over a period of years.
Proceedings ArticleDOI
DR-TANet: Dynamic Receptive Temporal Attention Network for Street Scene Change Detection
TL;DR: Li et al. as mentioned in this paper proposed the temporal attention and explored the impact of the dependency-scope size of temporal attention on the performance of change detection, achieving state-of-the-art performance on street scene datasets.
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Incorporating global-local a Priori knowledge into expectation-maximization for SAR image change detection
TL;DR: An unsupervised change-detection approach for multitemporal SAR images that specifies a priori knowledge about the spatial characteristics of the classes through Dempster-Shafer evidence theory and embeds it into the Expectation-Maximization (EM) iteration process.
Proceedings ArticleDOI
Practical Considerations in Unsupervised Change Detection Using SAR Images
Bulent Ayhan,Chiman Kwan +1 more
TL;DR: It was observed that effective change detection using SAR images require good denoising and post-processing algorithms in order to achieve decent performance.
Journal ArticleDOI
Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
Shimin Tang,Zhiqiang Chen +1 more
TL;DR: It is concluded that hazard types are more identifiable than damage levels in disaster-scene images, and the treatment of hazard-agnostic damage leveling further contributes to the underlying uncertainties.
References
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Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Journal ArticleDOI
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
Stuart Geman,Donald Geman +1 more
TL;DR: The analogy between images and statistical mechanics systems is made and the analogous operation under the posterior distribution yields the maximum a posteriori (MAP) estimate of the image given the degraded observations, creating a highly parallel ``relaxation'' algorithm for MAP estimation.
Book
Multiple view geometry in computer vision
Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Journal ArticleDOI
Fundamentals of statistical signal processing: estimation theory
TL;DR: The Fundamentals of Statistical Signal Processing: Estimation Theory as mentioned in this paper is a seminal work in the field of statistical signal processing, and it has been used extensively in many applications.
Journal ArticleDOI
Image registration methods: a survey
Barbara Zitová,Jan Flusser +1 more
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.