Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-View Large-Scale Bundle Adjustment Method for High-Resolution
  Satellite Images   Given enough multi-view image corresponding points (also called tie points)
and ground control points (GCP), bundle adjustment for high-resolution
satellite images is used to refine the orientations or most often used
geometric parameters Rational Polynomial Coefficients (RPC) of each satellite
image in a unified geodetic framework, which is very critical in many
photogrammetry and computer vision applications. However, the growing number of
high resolution spaceborne optical sensors has brought two challenges to the
bundle adjustment: 1) images come from different satellite cameras may have
different imaging dates, viewing angles, resolutions, etc., thus resulting in
geometric and radiometric distortions in the bundle adjustment; 2) The
large-scale mapping area always corresponds to vast number of bundle adjustment
corrections (including RPC bias and object space point coordinates). Due to the
limitation of computer memory, it is hard to refine all corrections at the same
time. Hence, how to efficiently realize the bundle adjustment in large-scale
regions is very important. This paper particularly addresses the multi-view
large-scale bundle adjustment problem by two steps: 1) to get robust tie points
among different satellite images, we design a multi-view, multi-source tie
point matching algorithm based on plane rectification and epipolar constraints,
which is able to compensate geometric and local nonlinear radiometric
distortions among satellite datasets, and 2) to solve dozens of thousands or
even millions of variables bundle adjustment corrections in the large scale
bundle adjustment, we use an efficient solution with only a little computer
memory. Experiments on in-track and off-track satellite datasets show that the
proposed method is capable of computing sub-pixel accuracy bundle adjustment
results.

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The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
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The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
2
The categories of this paper are: math
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat