Special Issue "Remote Sensing Big Data: Theory, Methods and Applications"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (31 May 2017)
Prof. Liping Di
Department of Geography and Geoinformation Science, Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030 USA
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Interests: agro-geoinformatics, geospatial cyberinfrastructure; standards and interoperability; digital agriculture; big data; geospatial cloud; web services; geospatial knowledge system; agricultural decision systems; global climate change; remote sensing; geographic information system
Assoc. Prof. Peng Liu
Prof. Dr. Lizhe Wang
We have entered an era of big data. Our ability to acquire remote sensing data has been improved to an unprecedented level. For a large ground station (e.g., China Remote Sensing Satellite Ground Station (RSGS)), the volume of global data archive could be on the Exabyte level.
When characterizing big data, it is popular to refer to the 3Vs, i.e., remarkable growths in Volume, Velocity and Variety of data. However, this statement is too general, and big data can also be referred to data from different sources, such as medical data, internet data, remote sensing data, etc. Remote sensing data often differ in resolutions, revisit cycle, spectrum, and mode of imaging. Therefore, we can choose different remote sensing systems and datasets for different applications. For remote sensing big data, the 3Vs could be more concretely extended to characteristics of multi-source, multi-scale, high-dimensional, dynamic-state, isomer, and non-linear characteristics.
It is important for us to consider these more concrete and particular characteristics of remote sensing big data when using remote sensing to extract information and understand geo-processes. These characteristics are fundamental assumptions and priors when we analyze remote sensing big data. More characteristics could provide us more information. However, there is no doubt that most of existing techniques and methods are too limited to solve all the problems of remote sensing big data due to its complexity. Since almost all algorithms and models have to consider the intrinsic and extrinsic characteristics of data, most of the fundamental theories, methods and even applications now have to adapt to the great changes from remote sensing big data. It is both the opportunity and challenge for remote sensing communities.
With these issues in mind, it is time to present the current state-of-the-art theoretical, methodological, and applicational research on remote sensing big data. The topics of interest include, but are not limited to:
• 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
Assoc. Prof. Peng Liu
Prof. Lizhe Wang
Manuscript Submission Information
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