Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Wan, Xiaoqing;Chen, Feng;Gao, Weizhe;Mo, Dongtao;Liu, Hui

通讯作者：

Wan, XQ

作者机构：

[Mo, Dongtao; Chen, Feng; Gao, Weizhe; Liu, Hui; Wan, Xiaoqing] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.

[Wan, Xiaoqing] Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.

通讯机构：

[Wan, XQ ] H

Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.

Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.

语种：

英文

关键词：

Circulant singular spectrum analysis (CiSSA);Decision fusion;Hyperspectral image (HSI);Local ternary pattern (LTP);Pattern classification

期刊：

Scientific Reports

ISSN：

2045-2322

年：

2025

卷：

期：

页码：

6972

DOI：

10.1038/s41598-025-90926-z

基金类别：

Scientific Research Fund of Hunan Provincial Education Department#&#&#23B0666#&#&#23B0666#&#&#23B0666#&#&#23B0666#&#&#23B0666 Science and Technology Plan Project of Hunan Province#&#&#2016TP1020#&#&#2016TP1020#&#&#2016TP1020#&#&#2016TP1020#&#&#2016TP1020 14th Five-Year Plan Key Disciplines and Application-oriented Special Disciplines of Hunan Province#&#&#Xiangjiaotong [2022] 351#&#&#Xiangjiaotong [2022] 351#&#&#Xiangjiaotong [2022] 351#&#&#Xiangjiaotong [2022] 351#&#&#Xiangjiaotong [2022] 351 Science Foundation of Hengyang Normal University#&#&#2022QD07#&#&#2022QD07#&#&#2022QD07#&#&#2022QD07#&#&#2022QD07 Hengyang City Guidance Plan Project#&#&#202323036897#&#&#202323036897#&#&#202323036897#&#&#202323036897#&#&#202323036897

机构署名：

本校为第一且通讯机构

院系归属：

计算机科学与技术学院

摘要：

Hyperspectral images (HSIs) contain rich spectral and spatial information, motivating the development of a novel circulant singular spectrum analysis (CiSSA) and multiscale local ternary pattern fusion method for joint spectral-spatial feature extraction and classification. Due to the high dimensionality and redundancy in HSIs, principal component analysis (PCA) is used during preprocessing to reduce dimensionality and enhance computational efficiency. CiSSA is then applied to the PCA-reduced images for robust spatial pattern extraction via circulant matrix decomposition. The spatial features ...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Fusion of circulant singular spectrum analysis and multiscale local ternary patterns for effective spectral-spatial feature extraction and small sample hyperspectral image classification

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问