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MLRS 2016 : Advanced Machine Learning Methods for Remote Sensing Image Processing

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Link: http://www.springer.com/cda/content/document/cda_downloaddocument/Advanced+Machine+Learning+Methods+for+Remote+Sensing+Image+Processing_revised_Liu.pdf?SGWID=0-0-45-1538971-p355382
 
When Jun 1, 2016 - Aug 1, 2016
Where non
Submission Deadline Aug 1, 2016
Notification Due Nov 1, 2016
Final Version Due Mar 3, 2017
 

Call For Papers

Guest Editors:
Peng Liu, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.R. China, liupeng@radi.ac.cn
Lizhe Wang, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, P.R. China, Lizhe.Wang@computer.org

CALL FOR PAPERS
We have entered an era of big data. The requirements of different investigations have increased the specialization and diversity of techniques of acquiring remote sensing image. We can take them as a new type of multimedia data. Remote sensing image often differ features in terms of their resolution, revisit cycle, spectrum, and mode of imaging. It is popular to refer to the three Vs when characterizing big data: remarkable growths in the volume, velocity and variety of data. However, remote sensing image big data has several concrete and special characteristics: multi-source, multi-scale, high dimensional, dynamic-state, isomer, and non-linear characteristics. There is no doubt that existing techniques and methods are too limited to solve all the problems of remote-sensing image data completely. Therefore, more advanced methods are required to find the implicit law hidden within the remote-sensing image.
In recent years, machine learning methods play an essential role in the data analysis of remote sensing, including image classification, image segmentation, registration and fusion, target detection, information retrieval, etc. Due to large variations and complexity of the in space and time, it is typically difficult to derive analytical solutions or formulations to represent characteristic and changing in the surface of the earth based on remote sensing data. Researchers are now beginning to adapt advanced modern machine learning and pattern recognition techniques, such as manifold learning, sparse representation, low-rank presentation, compressive sensing and deep learning, to solve related problems in the complex remote sensing data. The main scope of this special issue is to help boost the scientific research within the broad field of machine learning in the remote sensing image data. This special issue will focus on major trends and challenges in this area, and will present work aimed to identify new cutting-edge research and their applications in remote sensing images.
The topics of interest for this special issue include, but are not limited to:
• Machine Learning theories such as SVM, low rank, compressive sensing, sparse coding, transfer learning and deep learning
• Correlativity analysis for remote sensing image
• Correlativity analysis for GIS data
• Reconstruction of remote sensing image
• Fusion of remote sensing data
• Classification based on remote sensing image
• Change detection based on spatial-temporal remote sensing image
• Sparse representation of remote sensing image
• Dimension reduction for remote sensing data
• High performance computing for complex remote sensing data
• Computing vision and target detection based on remote sensing image
• Applications based on remote sensing image, such as agriculture, environment, land cover, hydrology and etc.
• Data visualization and analysis for GIS and remote sensing data

Important Dates:
Open for submissions: Already Open
Closed for submissions: Aug. 1st, 2016
Results of first round of reviews: Nov. 1st, 2016
Submission of revised manuscripts: Jan. 1st, 2017
Final decision: Feb. 31th, 2017

Paper Submission
Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by, other journals. Springer offers authors, editors and reviewers of Multimedia Tools and Applications journal a web-enabled online manuscript submission and review system. Our online system offers authors the ability to track the review process of their manuscript. Manuscripts should be submitted to: http://MTAP.edmgr.com. This online system offers easy and straightforward log-in and submission procedures, and supports a wide range of submission file formats. Please login the submission system, enter the "Select Article Type" menu, and then select item of "Machine Learning for RS image Processing".
All papers will be peer-reviewed following the Multimedia Tools and Applications reviewing procedures. Authors should prepare their manuscripts according to the online submission page of Multimedia Tools and Applications at www.Springer.com/11042

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