Comparing Area-Based and Feature-Based Methods for Co-Registration of Multispectral Bands on GPU

Ordóñez, Álvaro; Heras, Dora B.; Argüello, Francisco

doi:10.1109/igarss47720.2021.9554152

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The extraordinary evolution in this aspect during the last few years has motivated the extended use of GPUs for accelerating many different RS and, in particular, hyperspectral imaging-related tasks [74]- [76], [100], [102], [103]. These include registration [14], [104], segmentation [105], classification [76], or change detection [106], among others.…”

Section: Specialized Hardware Computingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

Self Cite

View full text Add to dashboard Cite

T he High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recently established under the IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics (ESI) Technical Committee to connect a community of interdisciplinary researchers in remote sensing (RS) who specialize in advanced computing technologies, parallel programming models, and scalable algorithms. HDCRS focuses on three major research topics in the context of RS: 1) supercomputing and distributed computing, 2) specialized hardware computing, and 3) quantum computing (QC). This article presents these computing technologies as they play a major role for the development of RS applications. The HDCRS disseminates information and knowledge through educational events and publication activities which will also be introduced in this article.

show abstract

Section: Specialized Hardware Computingmentioning

confidence: 99%

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Cavallaro

Heras

et al. 2022

IEEE Geosci. Remote Sens. Mag.

Self Cite

View full text Add to dashboard Cite

show abstract

Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence

Meoni,

Prete,

Serva

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

From Fields to Pixels: UAV Multispectral and Field-Captured RGB Imaging for High-Throughput Wheat Spike and Kernel Counting

Mohammed,

Ali,

Bais

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Comparing Area-Based and Feature-Based Methods for Co-Registration of Multispectral Bands on GPU

Abstract: This a post-print of the article "Comparing Area-Based and Feature-Based Methods for Co-Registration of Multispectral Bands on GPU" published in the

Cited by 3 publications

References 5 publications

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

High-Performance and Disruptive Computing in Remote Sensing: HDCRS—A new Working Group of the GRSS Earth Science Informatics Technical Committee [Technical Committees]

Unlocking the Use of Raw Multispectral Earth Observation Imagery for Onboard Artificial Intelligence

From Fields to Pixels: UAV Multispectral and Field-Captured RGB Imaging for High-Throughput Wheat Spike and Kernel Counting

Contact Info

Product

Resources

About