Hyperspectral feature mapping classification based on mathematical morphology

Chang Liu; Junwei Li; Guangping Wang; Jingli Wu

doi:10.1117/12.2268431

8 March 2017 Hyperspectral feature mapping classification based on mathematical morphology

Chang Liu; Junwei Li; Guangping Wang; Jingli Wu

Author Affiliations +

Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 102552A (2017) https://doi.org/10.1117/12.2268431
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China

Abstract

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm’s performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

Citation Download Citation

Chang Liu, Chang Liu, Junwei Li, Junwei Li, Guangping Wang, Guangping Wang, Jingli Wu, Jingli Wu, } "Hyperspectral feature mapping classification based on mathematical morphology", Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 102552A (8 March 2017); doi: 10.1117/12.2268431; https://doi.org/10.1117/12.2268431

ACCESS THE FULL ARTICLE

PERSONAL SIGN IN
Full access may be available with your subscription

Email or Username Forgot your username?

Password Forgot your password?

Show

Remember Email/Username on this computer

Remember Password

No SPIE account? Create an account
or Institutional Sign In via Shibboleth

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $14.40

Non-members: $18.00 ADD TO CART

Show All Keywords

Keywords/Phrases

Search In:

Publication Years