Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolution Based Spectral Partitioning Architecture for Hyperspectral
  Image Classification   Hyperspectral images (HSIs) can distinguish materials with high number of
spectral bands, which is widely adopted in remote sensing applications and
benefits in high accuracy land cover classifications. However, HSIs processing
are tangled with the problem of high dimensionality and limited amount of
labelled data. To address these challenges, this paper proposes a deep learning
architecture using three dimensional convolutional neural networks with
spectral partitioning to perform effective feature extraction. We conduct
experiments using Indian Pines and Salinas scenes acquired by NASA Airborne
Visible/Infra-Red Imaging Spectrometer. In comparison to prior results, our
architecture shows competitive performance for classification results over
current methods.

24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex