Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar
  Image Formation   Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar
signals from a moving object, such as a satellite, towards the target of
interest. Reflected radar echoes are received and later used by image formation
algorithms to form a SAR image. There is great interest in using SAR images in
computer vision tasks such as classification or automatic target recognition.
Today, however, SAR applications consist of multiple operations: image
formation followed by image processing. In this work, we train a deep neural
network that performs both the image formation and image processing tasks,
integrating the SAR processing pipeline. Results show that our integrated
pipeline can output accurately classified SAR imagery with image quality
comparable to those formed using a traditional algorithm. We believe that this
work is the first demonstration of an integrated neural network based SAR
processing pipeline using real data.

12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc