Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Deep Learning for Classification of Hyperspectral Images   Active deep learning classification of hyperspectral images is considered in
this paper. Deep learning has achieved success in many applications, but
good-quality labeled samples are needed to construct a deep learning network.
It is expensive getting good labeled samples in hyperspectral images for remote
sensing applications. An active learning algorithm based on a weighted
incremental dictionary learning is proposed for such applications. The proposed
algorithm selects training samples that maximize two selection criteria, namely
representative and uncertainty. This algorithm trains a deep network
efficiently by actively selecting training samples at each iteration. The
proposed algorithm is applied for the classification of hyperspectral images,
and compared with other classification algorithms employing active learning. It
is shown that the proposed algorithm is efficient and effective in classifying
hyperspectral images.

12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas