Analysis of Big Data in Remote Sensing

Dear Colleagues,

Big data is a very important topic in many research areas. Every day a large number of Earth observation (EO) space borne and airborne sensors from many different countries provide a massive amount of remotely-sensed data. Those data sets comprise different spectral bandwidths (dimensionality), spatial resolutions, and radiometric resolutions. The current estimations are that remotely sensed data are now being collected following a Petabyte level growth per day over the world. Combined with human activities and data from social science, the massive remotely sensed data (which consists of big data in remote sensing) have been successfully used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc.

This Special Issue on “Analysis of Big Data in Remote Sensing” is intended to introduce the latest techniques to analyze big data in remote sensing applications. The Special Issue is expected to bring together experts from different research areas to discover and realize the values of big data in various remote sensing areas. As a result, different analysis techniques exploiting big data will be collected in the Special Issue, which will provide a first necessary effort towards the incorporation of this technology into the remote sensing field and also help academia, governments, and industries to gain insights into the potential of using big data techniques and concepts in remote-sensing applications.

High-quality contributions with emphasis placed on (but not limited to) the topic areas listed below will be solicited for the Special Issue for the analysis of big data in remote sensing using:

• Active learning
• Cloud computing
• Crowdsourcing
• Deep ensemble learning
• Deep fusion learning
• Deep reinforcement learning
• Fusion of deep and shallow machine learning
• High performance computing
• Representation learning
• Semi supervised deep learning
• Supercomputing
• Supervised deep learning
• Transfer deep learning
• Unsupervised deep learning

Prof. Jon Atli Benediktsson
Prof. Mingmin Chi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.