Distributed Computing for Remotely Sensed Big Data Processing

Volume 109, Issue 8 | August 2021

Guest Editors: Jón Atli Benediktsson and Zebin Wu

Special Section Papers

Scanning the Section

By J. A. Benediktsson and Z. Wu

Recent Developments in Parallel and Distributed Computing for Remotely Sensed Big Data Processing

By Z. Wu, J. Sun, Y. Zhang, Z. Wei, and J. Chanussot

A comprehensive review of state-of-the-art methods for processing remotely sensed big data is given along with a thorough investigation of existing distributed and parallel approaches that are based on popular high-performance computing (HPC) platforms. Future directions for tackling challenging issues in distributed and parallel processing of remotely sensed big data are given.

Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery

By Q. Du, B. Tang, W. Xie, and W. Li

Representative and recent advances in hyperspectral anomaly detection approaches are discussed along with their parallel and distributed implementations on graphic processing unit (GPU), cloud computing, and field-programmable gate array (FPGA) platforms.

Distributed Deep Learning for Remote Sensing Data Interpretation

By J. M. Haut, M. E. Paoletti, S. Moreno-Álvarez, J. Plaza, J.-A. Rico-Gallego, and A. Plaza

A comprehensive review of the state-of-the-art in deep learning for remote sensing data interpretation is given. The pros and cons of the most widely used techniques in the literature are analyzed, as well as their parallel and distributed implementations. The article concludes with some remarks about future challenges in the application of deep learning techniques to distributed remote sensing data interpretation problems.

Distributed Fusion of Heterogeneous Remote Sensing and Social Media Data: A Review and New Developments

By J. Li, Z. Liu, X. Lei, and L. Wang

Distributed computing strategies in remote sensing techniques and applications that use various data sources are comprehensively reviewed. A new distributed fusion framework that can accelerate the fusion of heterogeneous remote sensing and social media data is proposed by decomposing large data sets into small ones and processing them in parallel.

Regular Papers

Review of Nanocomposite Dielectric Materials With High Thermal Conductivity

By M. Lokanathan, P. V. Acharya, A. Ouroua, S. M. Strank, R. E. Hebner, and V. Bahadur

This article summarizes progress in the development of such materials with a focus on developments that show promise for improved practical dielectrics.

Spintronics for Energy-Efficient Computing: An Overview and Outlook

By Z. Guo, J. Yin, Y. Bai, D. Zhu, K. Shi, G. Wang, K. Cao, and W. Zhao

This article reviews existing technology and provides a roadmap of spintronic devices for future energy-efficient computing and its relevant integration architectures.