Call for Papers: IEEE Geoscience and remote sensing magazine
Call for Papers: IEEE Geoscience and remote sensing magazine
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征文：IEEE地球科学与遥感杂志
DOI：
10.1109/mgrs.2014.2367411
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发表时间：
2023
影响因子：
14.6
通讯作者：
冨岡 亮太
中科院分区：
文献类型：
--
作者：
冨岡 亮太
“遥感数据融合”特刊 数据融合是遥感图像分析快速发展的领域之一。为了满足社会的需求，必须融合来自不同传感器、不同分辨率和不同质量的数据，这需要终端用户产品反映自然空间、多尺度、随时间演变并以不连续频率观察的环境问题。本期特刊将呈现一系列有关遥感数据融合最新进展的概述和教程式论文。该特刊的贡献重点将是回顾当前进展，强调文献中提出的满足多感官处理需求的最新趋势，并指出应对最新发射（或即将发射）任务所带来的信息洪流的策略。将特别关注多分辨率、多传感器和多时态处理的问题，同时仍然涵盖缺失数据重建和物理模型数据同化的问题。与杂志的方法和风格一致，特刊的撰稿人将高度重视调整讨论水平，以在确保科学深度和向包括遥感科学家、从业者和学生以及非数据融合专家在内的广大公众传播之间进行正确的权衡。
Special issue on " Data fusion in remote sensing " Data fusion is one of the fast moving areas of remote sensing image analysis. Fusing data coming from different sensors, at different resolutions, and of different quality is compulsory to meet the needs of society, which requires end-user products reflecting environmental problems that are naturally spatial, multiscale, evolving in time and observed at a discontinuous frequency. This special issue will present a series of overview and tutorial-like papers about the latest advances in remote sensing data fusion. The focus of the contributions to the special issue will be on reviewing the current progress, on highlighting the latest trends that have been proposed in the literature to answer the needs of multisensory processing, and on pointing out the strategies to be thought to answer the information deluge which will come with the latest missions launched (or to be launched). Particular attention will be paid to the questions of multiresolution, multisensor, and multitemporal processing, while still covering the problems of missing data reconstruction and data assimilation with physical models. Consistently with the approach and style of the Magazine, the contributors to the special issue will pay strong attention to tuning the discussion level to a correct trade-off between ensuring scientific depth and disseminating to a wide public that would encompass remote sensing scientists, practitioners, and students, and include non-data-fusion specialists.