High Performance Computing of Remotely-Sensed Data
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7802
Special Issue Editors
Interests: high performance computing in remote sensing; LiDAR data processing; FPGA-based computing; real-time data processing; hardware-software codesign
Interests: HPC; parallel computing; heterogeneous systems; LiDAR data processing
Interests: HPC; parallel computing; LiDAR data processing; performance analysis of parallel applications
Interests: HPC; parallel computing; big data processing; cloud computing; LiDAR data processing
Special Issue Information
Dear Colleagues,
Remote sensing applications exploit high volumes of data which demand high computation and memory bandwidth resources. Many of these applications can benefit from high-performance computing infrastructures and distributed processing systems based on homogeneous and heterogeneous processing resources. Suitable parallel solutions can provide speedups of several orders of magnitude over conventional PCs, allowing to accelerate the application development and target practical applications needing a fast response. This increase of computing speed allows focusing on developing new real-time applications which usually need to be processed onboard, close to the acquisition equipment. In this case, the aforementioned computer systems are not suitable because of weight or power consumption constraints. Emerging computing systems like FPGA or low-power GPUs are candidates for real-time remote sensed data processing.
This Special Issue of Remote Sensing aims to present state-of-the-art research on high-performance computing of remotely-sensed data, including multispectral, hyperspectral, LiDAR, and photogrammetry, among others. Papers are solicited on, but not limited to, the following research topics:
- GPU-based accelerators and heterogeneous computing for remote sensing;
- Hardware–software codesign for real time data processing;
- Parallel computing approaches for nonstructured point clouds;
- High-performance computing for deep learning, machine learning, and artificial intelligence in remote sensing;
- Big data analytics for remotely sensed data;
- Real-time remote sensing applications;
- Edge and cloud computing for remote sensing.
Dr. David López Vilariño
Dr. Francisco Fernández Rivera
Dr. José Carlos Cabaleiro Domínguez
Dr. Tomás Fernández Pena
Guest Editor
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 100 words) can be sent to the Editorial Office for announcement on this website.
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.
Keywords
- GPU
- parallel computing
- real-time processing
- cloud computing
- heterogeneous computing
