Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A 3D 2D convolutional Neural Network Model for Hyperspectral Image
  Classification   In the proposed SEHybridSN model, a dense block was used to reuse shallow
feature and aimed at better exploiting hierarchical spatial spectral feature.
Subsequent depth separable convolutional layers were used to discriminate the
spatial information. Further refinement of spatial spectral features was
realized by the channel attention method, which were performed behind every 3D
convolutional layer and every 2D convolutional layer. Experiment results
indicate that our proposed model learn more discriminative spatial spectral
features using very few training data. SEHybridSN using only 0.05 and 0.01
labeled data for training, a very satisfactory performance is obtained.

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The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con