Feature Extraction Based on Tensor Modelling for Classification Methods

被引：0

作者：

Yan, Ronghua ^{[1
,3
]}

Peng, Jinye ^{[1
,2
]}

Ma, Dongmei ^{[4
]}

Wen, Desheng ^{[3
]}

Dong, Yingdi ^{[5
]}

机构：

[1] Northwestern Polytech Univ, Sch Elect & Informat, Xian, Shaanxi, Peoples R China

[2] Northwest Univ, Sch Informat & Technol, Xian, Shaanxi, Peoples R China

[3] Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian, Shaanxi, Peoples R China

[4] Xian Janssen Pharmaceut Ltd, Xian, Shaanxi, Peoples R China

[5] Xian Univ Architecture & Technol, Sch Informat & Control Engn, Xian, Shaanxi, Peoples R China

来源：

2017 INTERNATIONAL CONFERENCE ON THE FRONTIERS AND ADVANCES IN DATA SCIENCE (FADS) | 2017年

关键词：

feature extraction; tensor; within-class information; classification; remote sensing; DIMENSIONALITY REDUCTION; IMAGES; GRAPH;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Both spatial and spectral information is used when a hyperspectral image is modeled as a tensor. However, this model does not consider both the class and within-class information about the spectral features of ground objects. This means that further improving classification is very difficult. The authors propose that class information, within-class information, and pixels are selected to model a third-order tensor. The most important advantage of the proposed method is that all the pixels of one class are mapped to the same coefficient vector. Therefore, the within-class scatter is minimized, and the classification is improved when compared to the previous methods.

引用

页码：124 / 129

页数：6

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