In this study, a method for reducing the atmospheric effects on SAR interferometric products is proposed. The method exploits MODIS data, as well as the Saastamoinen model for the estimation of the atmospheric component and the generation of spatially continuous data for this component. Then it recovers the interferometric signal from delays caused by the atmospheric component, through the appropriate modelling of the interferometric phase.
Performance of the method depends on MODIS data resolution; however, it always improves results. Experiments showed that the accuracy of DEMs that are produced by interferometry is improved when the proposed method is applied.
The values of the unwrapped phase produced by interferometric pairs can be parameterized for phase components, such as height, atmospheric path delay and deformation term, and estimated through DInSAR techniques. In this study, a method is proposed which estimates the atmospheric path delay using a single interferometric pair and an atmospheric path delay estimator. The estimator relies on the minimization of the outage probability, which is the probability that the Mean Square Error (MSE) of the estimated atmospheric component exceeds a desired MSE value. Outage minimization is equivalent to the minimization of the MSE of the atmospheric component for a fixed outage probability. The minimization of the MSE of the atmospheric component is determined by the second-order statistics of the topography and atmospheric components. For a specific SAR image geometry, second-order statistics of the topography component are satisfactorily approximated by the mean squared height errors of a high quality InSAR DEM for various height and slope classes, whereas second-order statistics of the atmospheric component are approximated by the inverse coherence value of the dataset which provides the high quality InSAR DEM. The proposed approach is validated to real satellite images and meteorological measurements.
The accuracy of InSAR DEMs is affected by the temporal decorrelation of SAR images which is due to atmosphere, land
use/cover, soil moisture, and roughness changes. Elimination of the temporal decorrelation of the master and slave image
improves the DEMs accuracy. In this study, the Independent Component Analysis was applied before interferometric
process. It was observed that using three ICA entries, ICA independent sources can be interpreted as background and
changed images. ICA when performed on the master and slave images using the same couple of additional images
produces two background images which enable the production of high quality DEMs. However, limitations exist in the
proposed approach.
In this study, a method for reducing the filtering effects on the interferometric phase signal is proposed. Theoretical analysis showed that while noise reduction is maximized after filtering, the loose of interferometric phase signal is also maximized. This state has been also verified by observations on SAR interferometric data where pixels with high coherence value, which are assumed to contain a lot of information, presented lower coherence values after SAR image filtering.
The proposed method performs interferometric phase modeling. The method recovers the signal after the interferometric filtering for the pixels that loss of information is observed. The selection of these pixels is based on the decrease of their coherence value after the filtering. Signal recovery is associated to the preservation of the initial values for these pixels. Consequently, the method prevents the decrease of the coherence values for these pixels.
Performance of the method depends on the performance of the used filter; however, it always improves the interferometric results. Since the phase signal is the basis for the DEM production, its preservation improves all the steps of the interferometric procedure, especially the phase unwapping. Effects of the method on the final interferometric product, the DEM, are also evident.
The proposed method was evaluated using real interferometric data. Experiments showed that the applied filters within this study, did not always improve the accuracy of the produced DEM. Sub-images for which filtering does not improve their mean coherence value have been selected and the proposed method has been applied. For these sub-images, coherence values and RMS errors of the produced DEMs showed that the method improves the results of the interferometric procedure. It compensates the negative effects of the filtering for these sub-images and leads to the improvement of the DEM accuracy in the majority of the cases.
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