Unsupervised change detection for remotely sensed multi-spectral images based on context-aware saliency-spectral-spatial features and weighted coarse-to-fine fusion

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：

Liao, Juan;Zhang, Fulin;Cao, Jiannong;Wang, Kun;Wang, Lingyu;...

通讯作者：

Liao, J

作者机构：

[Liao, Juan; Liao, J] Hengyang Normal Univ, Natl Local Joint Engn Lab Digital Preservat & Inn, Hengyang, Peoples R China.

[Liao, Juan; Liao, J; Zhang, Fulin] Hengyang Normal Univ, Sch Geog & Tourism, Hengyang, Peoples R China.

[Liao, Juan; Liao, J; Zhang, Fulin] Hengyang Dayan Geog Informat Co Ltd, Hengyang, Peoples R China.

[Cao, Jiannong] Changan Univ, Sch Geol Engn & Surveying, Xian, Peoples R China.

[Wang, Kun] Shaanxi Prov Land Engn & Construct Grp, Xian, Peoples R China.

通讯机构：

[Liao, J ] H

Hengyang Normal Univ, Natl Local Joint Engn Lab Digital Preservat & Inn, Hengyang, Peoples R China.

Hengyang Normal Univ, Sch Geog & Tourism, Hengyang, Peoples R China.

Hengyang Dayan Geog Informat Co Ltd, Hengyang, Peoples R China.

语种：

英文

关键词：

Image segmentation;Image fusion;Remote sensing;Visualization;Feature extraction;Feature fusion;Principal component analysis;Critical dimension metrology;Scene classification;Machine learning

期刊：

Journal of Applied Remote Sensing

ISSN：

1931-3195

年：

2024

卷：

期：

DOI：

10.1117/1.JRS.18.046501

基金类别：

Excellent Youth Project of the Hunan Provincial Education Department [22B0725, 23B0677, 23B0670]; Research Initiation Project of Hengyang Normal University [2022QD15, 2022QD10, 2022QD01]; Open fund project of the National-Local Joint Engineering Laboratory on Digital Preservation and Innovative Technologies for the Culture of Traditional Villages and Towns [2022HSKFJJ003]; National Natural Science Foundation of China [42301419, 41571346]

机构署名：

本校为第一且通讯机构

院系归属：

数学与统计学院

摘要：

Saliency detection is a conventional computer vision technique used to identify salient regions in difference images for change detection (CD) in multi-temporal/bi-temporal remotely sensed images. However, most existing saliency-based CD methods have primarily focused on analyzing the colors and brightness of different images, often neglecting the rich spectral-spatial and context-aware features of remotely sensed multi-spectral images. Furthermore, these methods independently leverage the visual saliency features and spectral-spatial features of remote sensing images, without seamlessly integ...

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

Unsupervised change detection for remotely sensed multi-spectral images based on context-aware saliency-spectral-spatial features and weighted coarse-to-fine fusion

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