Abstract
Abstract. Multitemporal SAR images are a very useful source of information for a large amount of applications, especially for change detection and monitoring. In this paper, a new SAR change detection and monitoring approach is proposed through the analysis of a time series of SAR images covering the same region. The first step of the method is the SAR filtering preprocessing step using an extension of the spatial NL-means filter to the temporal domain. Then, the Rayleigh Kullback Leibler and the Rayleigh Distribution Ratio measures are combined to detect the changes between a reference image and each SAR image of the time series at both local and global scale. These measures are combined using the Dezert-Smarandache theory which takes into account conflicts between sources and thus enhances the dual change detection results. Finally, a pixel based temporal classification is applied starting from the obtained change maps in order to describe the temporal behaviour of the covered regions.
Highlights
Remote sensing applications have known a fast expansion thanks to the diversity and the amount of satellite images
These methods can be classified in two classes: approaches based on pixel intensity and approaches based on local statistics
The proposed approach was applied first on synthetic data which simulate most of the categories of the temporal changes and validated in terms of Overall Accuracy (OA) coefficient
Summary
Remote sensing applications have known a fast expansion thanks to the diversity and the amount of satellite images. SAR data change detection allows the analysis of land phenomenon for a large range of applications such as the urban and agriculture regions monitoring, the mapping of damages following a natural disaster, etc. These changes can be of different types, origins and durations. More information may be extracted from the comparison of the local probability density functions (pdfs).Once the pdfs parameters are estimated, their comparison can be performed using different criteria and the most usual one is the Kullback-Leibler divergence [4] All these techniques are designed for comparison between only two SAR images. Most of them are feature and model based Techniques, pixel based clustering techniques or frequent sequential pattern based techniques [6]
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