首页 | 本学科首页   官方微博 | 高级检索  
     

遥感图像时空融合综述
引用本文:杨广奇,刘慧,钟锡武,陈龙,钱育蓉. 遥感图像时空融合综述[J]. 计算机工程与应用, 2022, 58(10): 27-40. DOI: 10.3778/j.issn.1002-8331.2111-0131
作者姓名:杨广奇  刘慧  钟锡武  陈龙  钱育蓉
作者单位:1.新疆维吾尔自治区信号检测与处理重点实验室,乌鲁木齐 8300462.新疆大学 软件学院,乌鲁木齐 8300003.新疆大学 信息工程学院,乌鲁木齐 8300004.新疆大学 软件工程重点实验室,乌鲁木齐 830000
基金项目:新疆维吾尔自治区研究生创新项目;国家自然科学基金;数据驱动的中俄云计算共享平台建设项目
摘    要:高时空分辨率的遥感图像大数据在遥感领域发挥着重要作用.然而,由于技术上和预算上的限制等原因,目前单一的卫星传感器无法获取同时具有高空间分辨率和高时间分辨率的遥感影像.因此遥感图像时空融合技术被认为是解决时间分辨率和空间分辨率折衷问题的有效途径之一.随着深度学习在各领域的广泛应用,深度学习技术已经被证实是解决图像问题非常...

关 键 词:遥感图像  高空间分辨率  传统学习  深度学习  时空融合

Temporal and Spatial Fusion of Remote Sensing Images:A Review
YANG Guangqi,LIU Hui,ZHONG Xiwu,CHEN Long,QIAN Yurong. Temporal and Spatial Fusion of Remote Sensing Images:A Review[J]. Computer Engineering and Applications, 2022, 58(10): 27-40. DOI: 10.3778/j.issn.1002-8331.2111-0131
Authors:YANG Guangqi  LIU Hui  ZHONG Xiwu  CHEN Long  QIAN Yurong
Affiliation:1.Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Urumqi 830046, China2.College of Software, Xinjiang University, Urumqi 830000, China3.College of Information Science and Engineering, Xinjiang University, Urumqi 830000, China4.Key Laboratory of Software Engineering, Xinjiang University, Urumqi 830000, China
Abstract:Big data of remote sensing image with high temporal and spatial resolution plays an important role in remote sensing field. However, due to technique and budget constraints, a single satellite sensor cannot acquire remote sensing images with both high spatial resolution and high temporal resolution. Therefore, the temporal and spatial fusion technology of remote sensing image is regarded as one of the effective ways to solve the tradeoff between temporal resolution and spatial resolution. With the wide application of deep learning in various fields, deep learning technology has been proved to be a very effective method to solve image problems. According to the research results of scholars at home and abroad, the classical algorithm of remote sensing image spatiotemporal fusion is comprehensively summarized. Meanwhile, the research results of remote sensing image spatiotemporal fusion algorithm based on deep learning are analyzed, which are replicated on three datasets and the experimental results are analyzed, and the future of remote sensing image spatiotemporal fusion is prospected.
Keywords:remote sensing images   high spatial resolution   traditional learning   deep learning   spatiotemporal fusion  
本文献已被 万方数据 等数据库收录!
点击此处可从《计算机工程与应用》浏览原始摘要信息
点击此处可从《计算机工程与应用》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号