中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Modified Nonparametric Weighted Feature Extraction Algorithm

文献类型:SCI/SSCI论文

作者Cui L. L.; Li, G. S.; Ren, H. R.; He, L.; Liao, H. J.
发表日期2015
关键词Spectral pan-similarity measure (SPM) Euclidean distance (ED) Nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE) Nonparametric weighted feature extraction (NWFE) thematic classification accuracy
英文摘要Nonparametric weighted feature extraction (NWFE) has been proven to be a powerful feature extraction tool for hyperspectral data classification with a weight function based on Euclidean distance (ED). In this paper, we propose a modified algorithm referred to as nonparametric weighted spectral pan-similarity measure feature extraction (NWSPMFE). In NWSPMFE, ED is replaced by the spectral pan-similarity measure, and the weight function is redefined in scatter matrices for NWFE. The performance of NWSPMFE is evaluated by comparing it with principal component analysis (PCA) and NWFE in terms of overall accuracy and Kappa analysis based on two experiment datasets. The overall classification accuracies of PCA, NWFE, and NWSPMFE for D.C. Mall and Indian Pine datasets are 0.942, 0.949, 0.961 and 0.496, 0.665, 0.697, respectively. However, NWSPMFE's runtime is slightly longer than that of NWFE.
出处Journal of the Indian Society of Remote Sensing
43
1
69-78
收录类别SCI
语种英语
ISSN号0255-660X
源URL[http://ir.igsnrr.ac.cn/handle/311030/38579]  
专题地理科学与资源研究所_历年回溯文献
推荐引用方式
GB/T 7714
Cui L. L.,Li, G. S.,Ren, H. R.,et al. Modified Nonparametric Weighted Feature Extraction Algorithm. 2015.

入库方式: OAI收割

来源:地理科学与资源研究所

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