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Continuous anomaly detection in satellite image time series is important for studying spatial-temporal processes of land cover changes. In another study, we proposed a method based on Z-scores of Season-Trend model Residuals (Z-STR), which can continuously detects anomaly regions in image time series. However, Z-STR only detects anomalies with large shifts but cannot detect anomalies with small or...
Regional monitoring of land cover conversion of natural vegetation to new informal human settlements is essential when investigating the migration of people to urbanized cities. Detecting these new settlements require reliable change detection methods. A robust change detection metric can be derived by analyzing the area under the autocorrelation function for a time series. The time dependence on...
One of the main objective of data fusion is the integration of several acquisition of the same physical object, in order to build a new consistent representation that embeds all the information from the different modalities. In this paper, we propose the use of optimal transport theory as a powerful mean of establishing correspondences between the modalities. After reviewing important properties and...
Natural disasters or human activities, such as forest fire, flood, and deforestation, may lead to anomaly or disturbance of land cover. Continuous detection of anomalies is important for studying spatial-temporal processes of land cover changes. Although many time-series-analysis methods have been developed for change detection, to the best of our knowledge, few methods focus on continuously detecting...
This paper presents change detection using very high resolution SAR data. Small patches of SAR data were used for graphical Lasso based algorithm. The graphical Lasso for time series is defined as solution of an l1-regularized maximum likelihood problem. The optimization problem was solved using alternating direction method of multipliers (ADMM). The time series of patches was observed. The efficiency...
This paper presents a method for strong scatterers change detection in synthetic aperture radar (SAR) images based on a decomposition for multi-temporal series. The formulated decomposition model jointly estimates the background of the series and the scatterers. The decomposition model retrieves possible changes in scatterers and the date at which they occurred. An exact optimization method of the...
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