Automatic analysis of the difference image for unsupervised change detection

By Lorenzo Bruzzone, Diego Fernández-Prieto. IEEE Transactions on Geoscience and Remote Sensing, 2000

1,421 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Biomedical Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/9580ed1dc6d823b83b47f0f2d871b0430bbf83dc

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In Analytical Chemistry

In Biomedical Engineering

In Media Technology

Citations in the corpus

Forest change detection by statistical object-based method (2006, background): “Indeed, the huge amount of work that has been done in change-thresholding of the histograms of vegetation-difference-images clearly warrants the reduction of human intervention in the process (Bruzzone & Prieto, 2000; Fung & LeDrew, 1988; Jin & Sader, 2005; Le Hégarat-Mascle & Seltz, 2004).”
Machine Learning for Aerial Image Labeling (2013, methodology): “In early work on aerial image labeling it was common to use a single image for both training and testing [Bischof et al., 1993, Bruzzone and Prieto, 2000].”
A Split-Based Approach to Unsupervised Change Detection in Large-Size Multitemporal Images: Application to Tsunami-Damage Assessment (2007, background): “, generalized Kittler and Illingworth (KI) algorithm [7], [9], [10] and generalized Expectation–Maximization-based algorithm [8], [17], [18]).”
Fuzzy clustering algorithms for unsupervised change detection in remote sensing images (2011, methodology): “Change Vector Analysis - CVA) [5,61], (c) vegetation indices (Vegetation Index Differencing - VID) [61,64] etc.”
Land-use/land-cover change detection using improved change-vector analysis (2003, background): “Thisusually requires a more experienced image analyst and a long trial time (Bruzzone and Prieto, 2000).”
An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images (2002, methodology): “The present method, which is an extension to the parametric technique presented in [34], exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) pro-454 cedure [35] and the expectation-maximization (EM) algorithm [36]–[38].”
SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images (2020, methodology): “Pixel-based CD (PBCD) was employed by comparing pixel spectral or textual values of images acquired on different dates [8].”
Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art (2017, methodology): “In this case, classical techniques in multispectral imaging such as thresholding [292] or clustering [293] can still be considered, based on the computed magnitude of the SCVs.”
A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain (2007, methodology): “From the definition in (4) and following indications in [16], we expect that unchanged pixels have magnitude close to zero (often not exactly zero due to the presence of noise components), while changed pixels have magnitude far from zero.”
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, methodology): “IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE JUNE 2019150 algorithms, the EM-GM approach performed better than the other three methods when considering the ED-Mag image, whereas EM-RRM and FCM achieved better results on the IRMAD-Mag and SAM-SI images, respectively.”
An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images (2005, methodology): “In [6], two automatic techniques based on the Bayes theory for the analysis of the DI are proposed.”
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images (2018, background): “Generally, change detection algorithms could be divided into the following categories: 1) Image algebra methods mainly include image difference, image ratio, image regression, and change vector analysis [11], [12].”
Self-Supervised Representation Learning for Remote Sensing Image Change Detection Based on Temporal Prediction (2020, background): “The complex background caused by the complicated topography also impairs precise detection [5].”
Self-Supervised Change Detection in Multiview Remote Sensing Images (2021, background): “As for homogeneous multi-sensor images, Solano et al. integrated CVA into a general approach to perform change detection between multi-sensor very high resolution (VHR) remote sensing images [23].”
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model (2024, background): “Due to the increased heterogeneity within the same land-cover feature brought about by the increase in spatial resolution, the traditional pixel-based CD methods [7]–[9] are difficult to achieve satisfactory detection results.”
Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images (2019, methodology): “has been paid to the unsupervised methods [7]–[9].”
A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis (2007, methodology): “For interesting approaches in the field of unsupervised change image thresholding, the reader can refer to the works of Bruzzone and Fernández Prieto [10], [11], Bruzzone and Serpico [12] and Bazi et al. [13].”
End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++ (2019, background): “Numerous PBCD approaches have been proposed to exploit the spectral and textual features of pixels, such as image algebra-based methods [7], image transformation-based methods [8,9], image classification-based methods [10], and machine learning-based methods [11–13].”
A critical synthesis of remotely sensed optical image change detection techniques (2015, methodology): “Furthermore, PCA and MAD transformations are scene dependant and 330 may prove difficult to interpret (Carvalho Júnior et al., 2013; Lu et al., 2004; Warner et al., 2009).”
Earthquake Damage Assessment of Buildings Using VHR Optical and SAR Imagery (2010, methodology): “the error probability in the change detection process can be derived automatically [8] using an expectation-maximization procedure.”