Corpus ID: 49908740

Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images

  title={Coupled dictionary learning for unsupervised change detection between multi-sensor remote sensing images},
  author={Vinicius Ferraris and Nicolas Dobigeon and Yanna Cruz Cavalcanti and Thomas Oberlin and Marie Chabert},
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations, the only images available may be those acquired through sensors with different characteristics. This paper addresses the problem of unsupervisedly detecting changes between two observed images acquired by different sensors. These sensor dissimilarities introduce additional issues in the context of operational change… Expand
1 Citations
Robust fusion algorithms for unsupervised change detection between multi-band optical images - A comprehensive case study
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Coupled Dictionary Learning for Change Detection From Multisource Data
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An Approach for Unsupervised Change Detection in Multitemporal VHR Images Acquired by Different Multispectral Sensors
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A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors
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Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: A Fusion-Based Approach
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Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images
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Joint Dictionary Learning for Multispectral Change Detection
An improved sparse coding method for change detection that minimizes the reconstruction errors of the changed pixels without the prior assumption of the spectral signature, which can adapt to different data due to the characteristic of joint dictionary learning. Expand
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This thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability in multi-temporal HS (MTHS) images, and investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models. Expand
Comparison of similarity measures of multi-sensor images for change detection applications
A series of similarity measures for automatic change detection has been investigated and their general performance compared using optical and SAR images covering a period of about six years could observe that the considered change detection algorithms perform differently but that none of them permits an "absolute" measure of the changes independent of the sensor. Expand
A Markov random field model for classification of multisource satellite imagery
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