Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images

@article{Mercier2008ConditionalCF,
  title={Conditional Copulas for Change Detection in Heterogeneous Remote Sensing Images},
  author={Gr{\'e}goire Mercier and Gabriele Moser and Sebastiano Bruno Serpico},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2008},
  volume={46},
  pages={1428-1441}
}
A new preprocessing technique is presented in this paper to automatically highlight changes in multitemporal strongly heterogeneous remotely sensed images. The proposed technique is devoted to the case where the two acquisitions, before and after a given event, are significantly different, due, for instance, to different sensors, acquisition modalities, or climatic conditions. In a previous study, it was proven that the local statistics of the images acquired at the two dates could be used to… 
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A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images
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FOR HOMOGENEOUS AND HETEROGENEOUS IMAGES
A statistical model for detecting changes in remote sensing images has recently been proposed in (Prendes et al., 2014, 2015). This model is sufficiently general to be used for homogeneous images…
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REMOTE SENSING IMAGE REGRESSION FOR HETEROGENEOUS CHANGE DETECTION
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A framework, based on image regression, to perform change detection in heterogeneous multitemporal satellite images, indicates that random forests achieve good performance, are fast and robust to hyperparameters, whereas the homogeneous pixel transformation method can achieve better accuracy at the cost of a higher complexity.
Change Detection with Heterogeneous Remote Sensing Data: From Semi-Parametric Regression to Deep Learning
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The problem and the opportunities of change detection from multitemporal data acquired through heterogeneous modalities are addressed, and the methodological approaches proposed in the literature’ which range from earlier semi-parametric regression to current deep learning architectures are reviewed.
Heterogeneous Change Detection on Remote Sensing Data with Self-Supervised Deep Canonically Correlated Autoencoders
TLDR
A major novelty of the work consists in including a prior information model, used to make the method unsupervised, within a well-established algorithm such as the canonical correlation analysis, and in combining these with a deep learning framework to give rise to an image translation method able to compare heterogeneous images regardless of their highly different domains.
A Reliable Mixed-Norm-Based Multiresolution Change Detector in Heterogeneous Remote Sensing Images
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A novel and reliable unsupervised change detection method to analyze heterogeneous remotely sensed image pairs based on an imaging modality-invariant operator that detects at different scale levels the differences in terms of high-frequency pattern of each structural region existing in the two heterogeneous satellite images.
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