Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hyperspectral Images Classification Using Energy Profiles of Spatial and
  Spectral Features   This paper proposes a spatial feature extraction method based on energy of
the features for classification of the hyperspectral data. A proposed
orthogonal filter set extracts spatial features with maximum energy from the
principal components and then, a profile is constructed based on these
features. The important characteristic of the proposed approach is that the
filter sets coefficients are extracted from statistical properties of data,
thus they are more consistent with the type and texture of the remotely sensed
images compared with those of other filters such as Gabor. To assess the
performance of the proposed feature extraction method, the extracted features
are fed into a support vector machine (SVM) classifier. Experiments on the
widely used hyperspectral images namely, Indian Pines, and Salinas data sets
reveal that the proposed approach improves the classification results in
comparison with some recent spectral spatial classification methods.

13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph