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
2D building change detection from high resolution satelliteimagery: A two-step hierarchical method based on 3D invariant primitives
TL;DR: An automatic method for detecting changes in a 2D building database, starting from recent satellite images, is presented and the outcomes show the good performance of the system, especially in terms of completeness, robustness and transferability.
Journal ArticleDOI
Automatic Storm Damage Detection in Forests Using High-Altitude Photogrammetric Imagery
TL;DR: The objective in this study was to develop an automatic method for storm damage detection based on comparisons of digital surface models (DSMs), where the after-storm DSM was derived by automatic image matching using high-altitude photogrammetric imagery.
Proceedings ArticleDOI
Weakly Supervised Silhouette-based Semantic Scene Change Detection
TL;DR: This paper presents a novel semantic scene change detection scheme with only weak supervision that proposes a new siamese network structure with the introduction of correlation layer and creates a publicly available dataset for semantic change detection.
Journal ArticleDOI
Multi-Feature Object-Based Change Detection Using Self-Adaptive Weight Change Vector Analysis
Qiang Chen,Yunhao Chen +1 more
TL;DR: It is found that self-adaptive weight-change vector analysis had superior capabilities of object-based change detection compared with standard change vector analysis, yielding Kappa statistics of 0.7976 and 0.7508 for Cases 1 and 2, respectively.
Journal ArticleDOI
Neural Background Subtraction for Pan-Tilt-Zoom Cameras
Alessio Ferone,Lucia Maddalena +1 more
TL;DR: Experimental results on several real image sequences and comparisons with seven state-of-the-art methods demonstrate the accuracy of the proposed neural-based background subtraction approach to moving object detection for pan-tilt-zoom cameras.
References
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