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… 
View on IEEE
doi.org
144 Citations
Highly Influential Citations
9
Background Citations
43
Methods Citations
55
Results Citations
1

144 Citations

Citation Type

Citation Type

Unsupervised Change Detection in Heterogeneous Remote Sensing Imagery
TLDR
The main novelty driving this study is that the comparison of affinity matrices can be used to define crossdomain similarities based on pixel relations rather than the direct comparison of radiometry values, which helps to tackle the problem of unsupervised heterogeneous change detection.
Multimodal Change Detection Using a Convolution Model-Based Mapping
  • Redha Touati, M. Mignotte, M. Dahmane
  • Mathematics, Environmental Science
    2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)
  • 2019
TLDR
A convolution model is proposed whose purpose is to map the first image to the domain of the other image thus ensuring that the two satellite images share the same statistics and on which a classical monomodal change detection methods can then be applied.
A New Multivariate Statistical Model for Change Detection in Images Acquired by Homogeneous and Heterogeneous Sensors
TLDR
This paper proposes a new approach for similarity measurement between images acquired by heterogeneous sensors that exploits the considered sensor physical properties and specially the associated measurement noise models and local joint distributions.
A Bayesian Nonparametric Model Coupled with a Markov Random Field for Change Detection in Heterogeneous Remote Sensing Images
TLDR
A Bayesian nonparametric framework allowing us to deal with an unknown number of objects in analysis windows without specifying an upper bound for this number is introduced and a Markov random field is introduced to account for the spatial correlation between neighboring pixels.
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…
A new change detector in heterogeneous remote sensing imagery
TLDR
This paper presents a novel, reliable and simple change detection operator which is first based on a imaging modality-invariant operator that detects the common specific high-frequency pattern of each structural region existing in the two heterogeneous satellite images.
REMOTE SENSING IMAGE REGRESSION FOR HETEROGENEOUS CHANGE DETECTION
TLDR
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
TLDR
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
TLDR
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.
...
...

References

SHOWING 1-10 OF 38 REFERENCES
Unsupervised change detection methods for remote sensing images
An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Due to its simplicity, image…
Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images
TLDR
An interactive fuzzy fusion approach is proposed to provide end-users with a simple and easily understandable tool for tuning the change-detection results.
A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis
  • J. Inglada, G. Mercier
  • Mathematics, Environmental Science
    IEEE Transactions on Geoscience and Remote Sensing
  • 2007
TLDR
A new similarity measure for automatic change detection in multitemporal synthetic aperture radar images based on the evolution of the local statistics of the image between two dates, which allows a multiscale approach in the change detection for operational use.
Comparison of similarity measures of multi-sensor images for change detection applications
TLDR
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.
Application of log-cumulants to change detection on multi-temporal SAR images
  • F. Bujor, J. Nicolas, E. Trouvé, J. Rudant
  • Environmental Science, Mathematics
    IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)
  • 2003
TLDR
Two different approaches which take the specificity of speckle distributions into account are proposed to detect changing areas by detecting changes of the mean radiometry by applying in a temporal direction a conventional "edge detector".
Unsupervised Change Detection From Multichannel SAR Images
TLDR
An automatic unsupervised contextual change-detection method is proposed for two-date multichannel SAR images, by integrating a SAR-specific extension of the Fisher transform with a variant of the expectation-maximization algorithm and with Markov random fields.
The Multiscale Change Profile: A Statistical Similarity Measure for Change Detection in Multitemporal SAR Images
  • J. Inglada, G. Mercier
  • Environmental Science, Mathematics
    2006 IEEE International Symposium on Geoscience and Remote Sensing
  • 2006
TLDR
A new similarity measure for automatic change detection in multitemporal SAR images based on the evolution of the local statistics of the image between two dates which allows the design of change indicators which provide better results than a monoscale analysis.
Change detection techniques for ERS-1 SAR data
  • E. Rignot, J. V. Zyl
  • Environmental Science, Mathematics
    IEEE Trans. Geosci. Remote. Sens.
  • 1993
Several techniques for detecting temporal changes in satellite synthetic-aperture radar (SAR) imagery are compared, using both theoretical predictions and spaceborne SAR data collected by the first…
An unsupervised approach based on the generalized Gaussian model to automatic change detection in multitemporal SAR images
TLDR
Experiments carried out on two sets of multitemporal images acquired by the European Remote Sensing 2 satellite SAR sensor confirm the effectiveness of the proposed unsupervised approach, which results in change-detection accuracies very similar to those that can be achieved by a manual supervised thresholding.
Automatic analysis of the difference image for unsupervised change detection
TLDR
The authors propose two automatic techniques (based on the Bayes theory) for the analysis of the difference image that allow an automatic selection of the decision threshold that minimizes the overall change detection error probability under the assumption that pixels in the difference picture are independent of one another.
...
...