Synthetic aperture radar interferometry (InSAR) has become a useful technique to obtain information about the slant range structure of terrain. In the last years, there are many research in this topic. Most of all papers attempt to solved the interferometric phase noise in spatial domain and some in frequency domain.
The interferometric technique is based on taking two SAR images in complex. It can be taken from two slightly different positions of the same area, and the generating an interferogram. There are two ways to get the interferometric phase. The first way is single-pass interferometry, where the images are taken at the same time by two antennas separated by a baseline in the cross-talk direction (two-antennas). The second way is repeat-pass interferometry, where the platform carrying the sensor travels over the same area two times with slightly different paths (two-paths). The interferometric phase is due to the interaction between two SAR images.
The accuracy of the interferometric phase depends on different factors, but the most important is the coherence. The spatial decorrelation and the incident angle are parameters that reduce the coherence between the images. For repeat-pass interferometry, the temporal decorrelation is a source of coherence loss. Another parameter that affects the DEM quality is the baseline or separation between the antennas. The higher the baseline the noisiest the interferometric phase, as the speckle pattern is ore decorrelated between the images. The statistics of interferometric phase have been characterized by a probability density function (PDF) based on the circular Gaussian assumption. The interferometric phase PDF depends on the coherence and the number of looks. In order to denoise, the improvement in interferometric phase is based on improving the coherence between both images. And the standard deviation of interferometric phase PDF is also significant.
There exists several filters that remove the interferometric phase noise. The simplest one is the box car filter that makes a simple multilook or averaging. Another used filter is the two dimension Gaussian filter. These filters do not adjust to the noise level variability. All these filters have a common point : windowing processing (or family of windows).
There has been growing interest in despeckling SAR images using wavelet multiscale techniques recently. The speckle effect in SAR images is characterized as multiplicative random noise, whereas most of existing wavelet denoising algorithms were developed for additived white Gaussian noise (AWGN), as AWGN is common in imaging and sensing systems. In this study, we use the AWGN for the speckle noise to simulate interferometric phase.
When the problem of interferometric phase reduction is addressed, the following points have to be take into account: to maintain the spatial resolution of original image, avoid the phase jumps in order to make possible the unwrapping process, and keep the fringe pattern of interferometric phase and target information.
For the property of wavelet decomposition, we presented the wavelet transform to denoise the in interferometric phase in the complex domain. And also takes the spatial correlator into our algorism to preserve the target and edge information.
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