Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ground Truth Simulation for Deep Learning Classification of
  Mid-Resolution Venus Images Via Unmixing of High-Resolution Hyperspectral
  Fenix Data   Training a deep neural network for classification constitutes a major problem
in remote sensing due to the lack of adequate field data. Acquiring
high-resolution ground truth (GT) by human interpretation is both
cost-ineffective and inconsistent. We propose, instead, to utilize
high-resolution, hyperspectral images for solving this problem, by unmixing
these images to obtain reliable GT for training a deep network. Specifically,
we simulate GT from high-resolution, hyperspectral FENIX images, and use it for
training a convolutional neural network (CNN) for pixel-based classification.
We show how the model can be transferred successfully to classify new
mid-resolution VENuS imagery.

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The categories of this paper are: physics
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The categories of this paper are: math
24
The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
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The categories of this paper are: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con