期刊征稿(SCI-Index, 2017-05-31) "Remote Sensing Big Data: Theory, Methods and Applications"# DataSciences - 数据科学
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http://www.mdpi.com/journal/remotesensing/special_issues/rs_bigdata
• Fundamental theories for remote sensing data processing, such as
data representation, data clean, dimension reduction, feature selection,
compressive sensing, deep learning, regression, correlation analysis, data
organization and structure, etc.;
• Methods and techniques for collection, distribution, sharing, and
visualization of remote sensing big data;
• Remote sensing big data processing infrastructures and systems, such
as cloud computing, high performance computing, Web computing;
• Fusion and assimilation of remote sensing big data;
• Inverse problem and low level vision task with remote sensing big
data, such as image denoising, image restoration, image recovery,
hyperspectral image un-mixing, SAR image reconstruction, supper-resolution,
etc.;
• Middle level vision task with remote sensing big data, such as image
segmentation, image change detection, features extraction, image
registration, etc.;
• High level vision task with remote sensing big data, such as target
detection or tracking, classification of scenes, image retrieval, image
understanding and etc.;
• Applications of remote sensing big data (i.e., agriculture,
environment, land cover, hydrology, forest, carbon cycle, atmosphere, ocean,
Earth surface processes)
Prof. Liping Di
Prof. Qian Du
Prof. Lizhe Wang
Assoc. Prof. Peng Liu
• Fundamental theories for remote sensing data processing, such as
data representation, data clean, dimension reduction, feature selection,
compressive sensing, deep learning, regression, correlation analysis, data
organization and structure, etc.;
• Methods and techniques for collection, distribution, sharing, and
visualization of remote sensing big data;
• Remote sensing big data processing infrastructures and systems, such
as cloud computing, high performance computing, Web computing;
• Fusion and assimilation of remote sensing big data;
• Inverse problem and low level vision task with remote sensing big
data, such as image denoising, image restoration, image recovery,
hyperspectral image un-mixing, SAR image reconstruction, supper-resolution,
etc.;
• Middle level vision task with remote sensing big data, such as image
segmentation, image change detection, features extraction, image
registration, etc.;
• High level vision task with remote sensing big data, such as target
detection or tracking, classification of scenes, image retrieval, image
understanding and etc.;
• Applications of remote sensing big data (i.e., agriculture,
environment, land cover, hydrology, forest, carbon cycle, atmosphere, ocean,
Earth surface processes)
Prof. Liping Di
Prof. Qian Du
Prof. Lizhe Wang
Assoc. Prof. Peng Liu