Sparse-Based Classification with Sub-dictionary Division and Weighted Combination

DOI

10.2991/wartia-16.2016.308How to use a DOI?

Keywords

Sparse Representation, Classification, Sub-dictionary Division, Weighted Combination

Abstract

A novel classification approach for hyperspectral data based on sparse representation framework is proposed in this paper. This new method divides all spectrums of hyperspectral samples to sub-dictionaries based on spectral characteristics of the data themselves. Meanwhile, a weighting algorithm based on minimum residual is adopted to combine sub-dictionaries. Experimental results show that our proposal obtained higher accuracy comparing with that of a whole dictionary.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Junyan Wang
AU  - Chunmei Zhang
AU  - Dan Li
PY  - 2016/05
DA  - 2016/05
TI  - Sparse-Based Classification with Sub-dictionary Division and Weighted Combination
BT  - Proceedings of the 2016 2nd Workshop on Advanced Research and Technology in Industry Applications
PB  - Atlantis Press
SP  - 1516
EP  - 1522
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-16.2016.308
DO  - 10.2991/wartia-16.2016.308
ID  - Wang2016/05
ER  -