TY - JOUR
T1 - Multi-level fusion of graph based discriminant analysis for hyperspectral image classification
AU - Feng, Fubiao
AU - Ran, Qiong
AU - Li, Wei
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2017/11/1
Y1 - 2017/11/1
N2 - Based on the graph-embedding framework, sparse graph-based discriminant analysis (SGDA), collaborative graph-based discriminant analysis (CGDA) and low rankness graph based discriminant analysis (LGDA) have been proposed with different graph construction. However, due to the inherent characteristics of ℓ1-norm, ℓ2-norm and nuclear-norm, single graph may be not optimal in capturing global and local structure of the data. In this paper, a multi-level fusion strategy is proposed in combining the three graph construction methods: 1) multiple graphs-based discriminant analysis (MGDA) in feature level with adaptive weights; 2) decision level fusion with D-S theory (GDA-DS), followed by a typical support vector machine (SVM) classification. Experimental results on three hyperspectral images datasets demonstrate that results with the fused strategy prevails with better classification performance.
AB - Based on the graph-embedding framework, sparse graph-based discriminant analysis (SGDA), collaborative graph-based discriminant analysis (CGDA) and low rankness graph based discriminant analysis (LGDA) have been proposed with different graph construction. However, due to the inherent characteristics of ℓ1-norm, ℓ2-norm and nuclear-norm, single graph may be not optimal in capturing global and local structure of the data. In this paper, a multi-level fusion strategy is proposed in combining the three graph construction methods: 1) multiple graphs-based discriminant analysis (MGDA) in feature level with adaptive weights; 2) decision level fusion with D-S theory (GDA-DS), followed by a typical support vector machine (SVM) classification. Experimental results on three hyperspectral images datasets demonstrate that results with the fused strategy prevails with better classification performance.
KW - D-S evidence theory
KW - Dimensionality reduction
KW - Graph embedding
KW - Hyperspectral data
KW - Multi-level fusion
UR - http://www.scopus.com/inward/record.url?scp=85000885290&partnerID=8YFLogxK
U2 - 10.1007/s11042-016-4183-7
DO - 10.1007/s11042-016-4183-7
M3 - Article
AN - SCOPUS:85000885290
SN - 1380-7501
VL - 76
SP - 22959
EP - 22977
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 21
ER -