IEEE Geoscience and Remote Sensing Magazine - June 2021 - 36

noisy images. This is an interesting development, as learning
from real data facilitates avoiding a domain gap and
fully exploiting the data. However, more work is needed
on these emerging approaches since they still require either
multitemporal data or the careful modeling of prior knowledge
about images and the speckle process.
DATA AVAILABILITY
Usually, deep learning methods require very large amounts
of data to effectively train a network. For this reason, the
availability of data is an issue of primary importance. In
the past, the availability of SAR data was very limited:
some data sets were available only for certain areas and
upon payment, while in other cases their access was limited
due to confidentiality issues. Only recently has the
situation changed, and some large SAR data sets are now
widely available for free. This novel open data policy was
first introduced by the European Space Agency (ESA) with
the launch of the Copernicus program in 2014. As a consequence,
the imagery acquired by several SAR sensors can
now be easily found online. In Table 1, we list the main
repositories of SAR images that are publicly available and
can be accessed online, and we describe their main characteristics
and data release policies.
As discussed in the previous section, some deep learning
methods for SAR despeckling are trained on multitemporal
(a)
(b)
(c)
(d)
FIGURE 4. The domain gap artifacting due to supervised training with synthetic speckle in a TerraSAR-X image. (a) A single-look noisy
image. (b) Model-based denoising (SAR-BM3D [15]). (c) Supervised denoising (intelligent detection using a CNN [40]). (d) Self-supervised
denoising (Speckle2Void [41]). Notice the cartoon-like edges and hallucinations in the flat regions produced by the supervised method.
36
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IEEE Geoscience and Remote Sensing Magazine - June 2021

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