Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The application of Convolutional Neural Networks to Detect Slow,
  Sustained Deformation in InSAR Timeseries   Automated systems for detecting deformation in satellite InSAR imagery could
be used to develop a global monitoring system for volcanic and urban
environments. Here we explore the limits of a CNN for detecting slow, sustained
deformations in wrapped interferograms. Using synthetic data, we estimate a
detection threshold of 3.9cm for deformation signals alone, and 6.3cm when
atmospheric artefacts are considered. Over-wrapping reduces this to 1.8cm and
5.0cm respectively as more fringes are generated without altering SNR. We test
the approach on timeseries of cumulative deformation from Campi Flegrei and
Dallol, where over-wrapping improves classication performance by up to 15%. We
propose a mean-filtering method for combining results of different wrap
parameters to flag deformation. At Campi Flegrei, deformation of 8.5cm/yr was
detected after 60days and at Dallol, deformation of 3.5cm/yr was detected after
310 days. This corresponds to cumulative displacements of 3 cm and 4 cm
consistent with estimates based on synthetic data.

11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph