Interferometric SAR Phase Denoising Using Proximity-Based K-SVD Technique

Abstract

This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in [ − π , π ] , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

“…The new high-frequency sub-band image is created by performing the logarithmic inverse transformation on the new dictionary and sparse matrix. Detailed theoretical information can be referred to in the literature [36]. Figure 5.…”

An Unsupervised Generative Neural Approach for InSAR Phase Filtering and Coherence Estimation

“…I NSAR or Interferometric Synthetic Aperture Radar is an emerging, highly successful remote sensing technique for measuring several geophysical quantities like surface deformation [1]. It is based on generating an interferogram as the complex difference of two SAR acquisitions of the same scene from slightly different view angles.…”

“…Thus phase filtering is preferred, even when it results in some decrease in resolution and increase in spatial correlation [3] and we need filters adapted to enhance phase rather than amplitude [4]. Filtering the real and imaginary parts of the complex phase in its wrapped form [5], [6] can avoid blurring edges, whereas unwrapping before filtering increases computation and potentially decreases accuracy [1]. Due to the non-stationary nature of InSAR signal, simple boxcar Manuscript submitted for review on D A T E. This work was supported by NSERC Discovery Grant and DND Supplement.…”

“…For similar computational concerns, we do not adopt strategies that are iterative [3], [4] (could also result in loss of detailed features [3]), multi-stage [29], [4] and require optimization during inference, e.g. via sparse coding [1].…”

Inverse Synthetic Aperture Radar Sparse Imaging Exploiting the Group Dictionary Learning

“…In contrast to dictionaries constructed with fixed image transformations used in sparse transformation based image formation models, the dictionaries obtained by the dictionary learning (DL) technology [10][11][12][13] are generated with the prior information of the unknown target image. Thus, the learned dictionaries are adaptive to the target images to be reconstructed and can find the optimal sparse representation coefficients [14,15].…”