DOI: 10.1016/j.jag.2014.03.015
Scopus记录号: 2-s2.0-84904467508
论文题名: Sparse dimensionality reduction of hyperspectral image based onsemi-supervised local Fisher discriminant analysis
作者: Shao Z ; , Zhang L
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 31, 期: 1 起始页码: 122
结束页码: 129
语种: 英语
英文关键词: Analysis (SELF)
; Dimensionality reduction
; Semi-supervised local Fisher discriminant
; Sparsity preserving projections (SPP)
Scopus关键词: complementarity
; discriminant analysis
; image classification
; mapping
; multispectral image
; remote sensing
英文摘要: This paper presents a novel sparse dimensionality reduction method of hyperspectral image based on semi-supervised local Fisher discriminant analysis (SELF). The proposed method is designed to be especially effective for dealing with the out-of-sample extrapolation to realize advantageous complementarities between SELF and sparsity preserving projections (SPP). Compared to SELF and SPP, the method proposed herein offers highly discriminative ability and produces an explicit nonlinear feature mapping for the out-of-sample extrapolation. This is due to the fact that the proposed method can get an explicit feature mapping for dimensionality reduction and improve the classification performance of classifiers by performing dimensionality reduction. Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79704
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Recommended Citation:
Shao Z,, Zhang L. Sparse dimensionality reduction of hyperspectral image based onsemi-supervised local Fisher discriminant analysis[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,31(1)