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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Journal ArticleDOI
Machine learning paradigm for structural health monitoring
Yuequan Bao,Hui Li +1 more
TL;DR: Light is shed on principles for machine learning paradigm for structural health monitoring with some examples and the existing challenges and open questions in this field are reviewed.
Journal ArticleDOI
Labeled dataset for integral evaluation of moving object detection algorithms
TL;DR: A public, complete, compact, and well structured database, which allows to test moving object detection strategies and is suitable for strategies exclusively focused on the detection of moving objects and also for those that integrate tracking algorithms in their detection approaches.
Proceedings ArticleDOI
Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
Maria Papadomanolaki,Sagar Verma,Maria Vakalopoulou,Siddharth Gupta,Konstantinos Karantzalos +4 more
TL;DR: A novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling is presented.
Journal ArticleDOI
Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree
TL;DR: This work proposes to exploit the benefit of Sentinel-2 images to monitor urban areas and to update Copernicus Land services, in particular the High Resolution Layer imperviousness, using independent image classification and data fusion that are fused using the Dempster–Shafer theory.
Journal ArticleDOI
Wavelet Fusion on Ratio Images for Change Detection in SAR Images
TL;DR: A novel method based on wavelet fusion for change detection in synthetic aperture radar (SAR) images by using complementary information from mean-Ratio and log-ratio images to generate the difference image (DI).
References
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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.
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Journal ArticleDOI
Image registration methods: a survey
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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.