A Method of Interferogram Filtering Based on Local Signal-to-Noise Ratio

Abstract

The invention relates to a local signal to noise ratio-based interferogram filtering method. According to the method, interferograms in a sliding window are subjected to filtering sequentially by the sliding of the window in the interferograms, wherein a method for performing the filtering on the interferograms in the sliding window comprises the following steps of: decomposing interferometric phases of the interferograms in the sliding window to obtain estimation frequency components of the interferograms in the sliding window; calculating according to the interferometric phases and the estimation frequency components to obtain a local signal to noise ratio of the interferograms in the sliding window; calculating according to the local signal to noise ratio to obtain a filtering parameter; and performing the filtering on the interferograms in the sliding window according to the filtering parameter. By the method, the degree of local noise in the interferograms can be estimated accurately, and the loss of filtering to the phase information of the interferograms is reduced while the noise reduction of the conventional filtering method is provided, so that integral superiority is embodied.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a filtering method of an InSAR interferogram based on a local signal-to-noise ratio. Background technique [0002] Synthetic aperture radar interferometry (Interferometric Synthetic Aperture Radar, INSAR) is based on two SAR images in the same area as the basic data processing, by calculating the phase difference of the two SAR images, the interference image is obtained, and then unwrapped by phase, from A new space-to-earth observation technology for obtaining terrain elevation data in interference fringes. [0003] In the data processing flow of Synthetic Aperture Radar Interferometry (InSAR), filtering the interferogram is a very important step. As mentioned above, the interferogram is used for subsequent phase unwrapping, and the quality of the interferogram will affect the complexity of the subsequent phase unwrapping, which determines the accuracy of the digital elevation m...

AI Technical Summary

Problems solved by technology

However, the noise level in the local window of the interferogram is usually unknown, so how to select an accurate filtering parameter α to improve the adaptability and accuracy of frequency domain filtering is a problem worth studying

This is also the biggest flaw of the current Goldstein frequency domain filtering algorithm.

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