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
A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure
TL;DR: This paper presents a novel approach to unsupervised change detection in multispectral remote-sensing images by using a selective Bayesian thresholding for deriving a pseudotraining set that is necessary for initializing an adequately defined binary semisupervised support vector machine classifier.
Proceedings ArticleDOI
Robust monocular SLAM in dynamic environments
TL;DR: A novel prior-based adaptive RANSAC algorithm (PARSAC) is proposed to efficiently remove outliers even when the inlier ratio is rather low, so that the camera pose can be reliably estimated even in very challenging situations.
Journal ArticleDOI
Change detection based on deep feature representation and mapping transformation for multi-spatial-resolution remote sensing images
TL;DR: This paper presents a novel multi-spatial-resolution change detection framework, which incorporates deep-architecture-based unsupervised feature learning and mapping-based feature change analysis, and tries to explore the inner relationships between them by building a mapping neural network.
Journal ArticleDOI
Change Detection in Optical Aerial Images by a Multilayer Conditional Mixed Markov Model
Csaba Benedek,Tamás Szirányi +1 more
TL;DR: A probabilistic model is proposed for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions that integrates global intensity statistics with local correlation and contrast features.
Proceedings ArticleDOI
Static and Moving Object Detection Using Flux Tensor with Split Gaussian Models
TL;DR: A moving object detection system named Flux Tensor with Split Gaussian models (FTSG) that exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison is presented.
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