Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploiting multi-temporal information for improved speckle reduction of
  Sentinel-1 SAR images by deep learning   Deep learning approaches show unprecedented results for speckle reduction in
SAR amplitude images. The wide availability of multi-temporal stacks of SAR
images can improve even further the quality of denoising. In this paper, we
propose a flexible yet efficient way to integrate temporal information into a
deep neural network for speckle suppression. Archives provide access to long
time-series of SAR images, from which multi-temporal averages can be computed
with virtually no remaining speckle fluctuations. The proposed method combines
this multi-temporal average and the image at a given date in the form of a
ratio image and uses a state-of-the-art neural network to remove the speckle in
this ratio image. This simple strategy is shown to offer a noticeable
improvement compared to filtering the original image without knowledge of the
multi-temporal average.

17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat