Convolutional block attention module U-Net: a method to improve attention mechanism and U-Net for remote sensing images

IF 1.4 4区地球科学 Q4 ENVIRONMENTAL SCIENCES Journal of Applied Remote Sensing Pub Date : 2022-06-27 DOI:10.1117/1.jrs.16.026516

Y. Zhang, Jiayuan Kong, Sifang Long, Yuanhao Zhu, Fushuai He

引用次数: 5

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

卷积块注意模块U-Net:一种改进遥感图像注意机制和U-Net的方法

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Journal of Applied Remote Sensing 环境科学-成像科学与照相技术

CiteScore

3.40

自引率

11.80%

发文量

194

审稿时长

3 months

期刊介绍： The Journal of Applied Remote Sensing is a peer-reviewed journal that optimizes the communication of concepts, information, and progress among the remote sensing community.

期刊最新文献

Monitoring soil moisture in cotton fields with synthetic aperture radar and optical data in arid and semi-arid regions Cascaded CNN and global–local attention transformer network-based semantic segmentation for high-resolution remote sensing image Coastal chlorophyll-a concentration estimation by fusion of Sentinel-2 multispectral instrument and in-situ hyperspectral data Spectral index for estimating leaf water content across diverse plant species using multiple viewing angles Optimal band selection using explainable artificial intelligence for machine learning-based hyperspectral image classification