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星空地协同对地监测研究组

征稿:SATELLITE HYPERSPECTRAL REMOTE SENSING: ALGORITHMS AND APPLICATIONS

时间: 2020-12-29 08:03

作者: admin

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华东师范大学地理信息科学教育部重点实验室谭琨教授拟在国际光学工程学会(SPIE)旗下的Journal of Applied Remote Sensing期刊组织一期卫星高光谱遥感:算法与应用特刊,敬请赐稿。


出版日期15卷第3期

提交截止日期2021年4月1日



客座编辑

谭琨

地理信息科学教育部重点实验室

华东师范大学

中国

tankuncu@gmail.com

贾秀萍

新兰威尔士大学

澳大利亚

x.jia@adfa.edu.au

Antonio J. Plaza

埃斯特雷马杜拉大学

高光谱计算实验室

西班牙

aplaza@unex.es



专刊投稿范围

高光谱遥感是当前遥感科学中一个快速发展的领域。高光谱影像包含了详细光谱信息以及相邻区域内的空间关系,可表达多光谱或其他类型影像无法提供的特征。它能够捕捉表征场景物理和化学性质的光谱特征,为更好地理解和监测地表地物开辟了新的途径。但同时,相邻波段间的高相关性和信息冗余,也给卫星高光谱影像分析带来了巨大的挑战。近年来,人工智能、深度学习和弱监督学习等先进计算技术的发展,弥补了高光谱影像在时空连续性的不足,扩展和增强了高光谱遥感的应用方向和范围。

本专题旨在汇总卫星高光谱遥感应用的最新研究成果,如高分五号、ZY1-02D、PRISMA、DESIS、HySIS等。研究方向包括但不限于以下内容:


•土地覆盖分类

•高光谱遥感影像解混

•地物识别和异常探测

•大气遥感

•土壤环境遥感

•水体质量监测

•环境监测和污染检测

•辐射传输模型

•植被监测和健康评估

•高光谱遥感数据融合



如需投稿,请根据投稿须知准备稿件,并通过在线投稿系统(https://jars.msubmit.net)投稿。在论文中还应附上一封说明,简述工作的内容。论文将根据期刊的审理规则和流程进行同行评审。同行评审将在稿件提交后立即开始,在稿件提交后六周内会做出首次审稿决定。编辑和排版校样一经作者核实后立即发表。欢迎各位同行投稿!



Publication Date
Vol. 15, Issue 3
Submission Deadline
1 April 2021

Guest Editors
Kun Tan
Key Laboratory of Geographic Information Science (Ministry of Education)
East China Normal University
Shanghai, China
tankuncu@gmail.com

Xiuping Jia
University of New South Wales
School of Engineering and Information Technology
Canberra, Australia
x.jia@adfa.edu.au

Antonio J. Plaza
University of Extremadura
Hyperspectral Computing Lab
Department of Technology of Computers & Communication
Cáceres, Spain
aplaza@unex.es

Scope
Hyperspectral remote sensing is currently a fast-moving area of not only research but also remote sensing applications. The high spectral resolution in hyperspectral images and the spatial relationship between adjacent regions provide critical information that cannot be provided by multispectral or other types of images. Its ability to capture the physical and chemical properties of scene materials opens the way to a better understanding and monitoring of a large variety of land cover and land use. On the other hand, the high correlation between adjacent bands and increased data redundancy bring great challenges to the analysis of satellite hyperspectral images. Advanced computing techniques, such as artificial intelligence, deep learning, and weak-supervised learning, have been developed in recent years to overcome the deficiencies of hyperspectral images in space and time, and to enhance their applications.
In this special section, we aim to compile state-of-the-art research on the application of satellite hyperspectral remote sensing, such as GAOFEN-5, ZY1-02D, PRISMA, DESIS, HySIS etc. Potential topics include but are not limited to the following:
  • Land cover type classification
  • Hyperspectral remote sensing images unmixing
  • Object identification and anomaly detection
  • Atmospheric study
  • Environmental monitoring and pollution detection
  • Radiative transfer modeling
  • Vegetation monitoring and health assessment
  • Sensor and data fusion
To submit a manuscript for consideration, please prepare the manuscript according to the journal guidelines and submit the paper via the online submission system (https://jars.msubmit.net). A cover letter indicating that the submission is intended for this special section should be included with the paper. Papers will be peer reviewed in accordance with the journal’s established policies and procedures. Peer review will commence immediately upon manuscript submission, with a goal of making a first decision within six weeks of manuscript submission. The special section is opened online once a minimum of four papers have been accepted. Each paper is published as soon as the copyedited and typeset proofs are approved by the author.

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