IEEE Geoscience and Remote Sensing Magazine - June 2020 - 77

transformed-domain filters, NL filters, and others. We introduced the concepts behind these methods and surveyed
the strengths and limitations of each process, theoretically and experimentally. Research achievements in InSAR
phase denoising look exciting; however, there is room for
improvement in fields such as NL filters and the upcoming
technology of newly advanced methods. In the following,

deep-learning methods, and their details are not included
in this article.
CONCLUSIONS AND PROSPECTS
In this article, we reviewed InSAR phase-denoising technology. During the past few decades, numerous methods
have been implemented, including traditional local filters,

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(b)

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FIGURE 13. The filtered images obtained using the sparse methods (RADARSAT-2 data). TV regularization [23]: (a) the filtered phase and

(b) the filtered amplitude. Wavelet regularization [100]: (c) the filtered phase and (d) the filtered amplitude.

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FIGURE 14. The filtered phase images obtained using sparse-wavelet regularization [100]. The (a) filtered phase, (b) phase difference,

A
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Area B Enlarged

Area A Enlarged

Interferometric
Phase

(c) residues (3,773), and (d) coherence of the filtered images.

(a)

(b)

(c)

(d)

(e)

FIGURE 15. The filtered phase images that were derived through various methods (Mount Etna data) [100]. (a) The original. (b) The Lee
filter [13]. (c) The wavelet filter [29]. (d) The region-growing method [60]. (e) The sparse-wavelet regularization [100].
JUNE 2020

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IEEE Geoscience and Remote Sensing Magazine - June 2020

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