Big Data in Earth Observation: A New Computing Paradigm for Remote Data Analysis

Dear Colleagues,

With the recent advances made in the Earth Observation (EO) field, the use of remote sensing information captured by available sensors (located on aerial and/or satellite platforms) has acquired a very important role in a wide range of human activities such as the management of environment and natural resources (including forests, water, geological and mineralogical resources), prevention of risks and catastrophes, planning of urban and rural spaces, detection of military objectives and intelligence tasks, among others. This has been fostered by the fact that a detailed characterization of the Earth's surface is now possible using the data collected by current remote sensing instruments for EO, which are able to collect data with higher spatial and spectral resolutions, thus allowing for the acquisition of a large variety of remotely sensed images, from panchromatic and RGB data to multispectral and hyperspectral scenes, from LiDAR and radar sensors, to thermal and optical images, and from low to medium, high and very high spatial resolutions.

For instance, the sensors capable of acquiring images with hundreds of spectral bands (called imaging spectrometers) are able to gather large amounts of information for the same area by recording hundreds of measurements in the spectral domain at different wavelengths. This allows "to see what the human eye cannot," making possible the generation of "data cubes," also known as hyperspectral images (HSI) with very large dimensionality. These images permit a very precise characterization of the terrestrial surface. For example, NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) sensor is able to capture HSI scenes with 224 spectral bands between 0.4 and 2.5 micrometers, and spatial resolution of about 20 meters per pixel. Such wealth of spatial and spectral information (despite imposing important computational requirements) has opened new possibilities in many applications, including the detailed characterization of agricultural and urban areas, or the monitoring and prevention of natural disasters such as forest fires, oil spills and other types of chemical pollution.

This Special Issue on “Big Data in Earth Observation: a new computing paradigm for remote data analysis" is intended to introduce the latest techniques in high performance computing (HPC) to the development and application of new image processing techniques for an adequate and computationally efficient exploitation of remotely sensed scenes from a Big Data point of view, exploring new computationally efficient models for extracting information from huge remote sensing datasets, with particular interest in the development of parallel and distributed techniques based on graphical processing units (GPUs) and grid/cloud computing platforms.

The goal of this Special Issue is to collect the latest and most advanced ideas regarding the new and efficient techniques for extracting information based on the new trends in advanced learning algorithms (including the newest machine and deep learning approaches).

Dr. Juan M. Haut
Ms. Mercedes E. Paoletti
Dr. Zebin Wu
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.