Review Article Digital change detection techniques using remotely-sensed data
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An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.Abstract:
A variety of procedures for change detection based on comparison of multitemporal digital remote sensing data have been developed. An evaluation of results indicates that various procedures of change detection produce different maps of change even in the same environment.read more
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Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics
TL;DR: In this paper, a case study has been conducted in the area of Avellino (Italy) to characterise the dynamics of changes during a fifty year period (1954-2004).
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Slow Feature Analysis for Change Detection in Multispectral Imagery
Chen Wu,Bo Du,Liangpei Zhang +2 more
TL;DR: This paper proposes a novel slow feature analysis (SFA) algorithm for change detection that performs better in detecting changes than the other state-of-the-art change detection methods.
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A comparison of time series similarity measures for classification and change detection of ecosystem dynamics
Stef Lhermitte,Stef Lhermitte,Jan Verbesselt,Willem Verstraeten,Willem Verstraeten,Pol Coppin +5 more
TL;DR: In this article, a quantitative comparison of the similarity measures in function of varying time series and ecosystem characteristics, such as amplitude, timing, and noise effects, is presented, and the performance of the commonly used similarity measures (D), ranging from Manhattan (DMan), Euclidean (DE) and Mahalanobis (DMah) distance measures, to correlation (DCC), Principal Component Analysis (PCA; DPCA) and Fourier based (DFFT,Dξ,DFk) similarities.
Journal ArticleDOI
Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data
TL;DR: Wang et al. as discussed by the authors proposed a new structural method based on road density combined with spectral bands for change detection, where road density represents one type of structural information while the multiple Landsat TM bands represent spectral information.
Journal ArticleDOI
Remote sensing of forest change using artificial neural networks
TL;DR: The results of the study indicate that the artificial neural network (ANN) estimates conifer mortality more accurately than the other approaches and offers a viable alternative for change detection in remote sensing.
References
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