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

Cavallaro, Gabriele; Heras, Dora B.; Wu, Zebin; Maskey, Manil; López, S.; Gawron, Piotr; Coca, Mihai; Datcu, Mihai

doi:10.1109/mgrs.2022.3145478

Cited by 6 publications

(2 citation statements)

References 137 publications

(118 reference statements)

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“…Cavallaro et al [34] present advanced High-Performance and Disruptive Computing (HDC) technologies in the context of i) supercomputing and distributed computing, ii) specialized hardware computing, and iii) Quantum Computing (QC) for specialized Parallel Programming Models (PPM) and scalable algorithms as they play a significant role in the advancements of RSBD applications. Geospatial Artificial Intelligence (geoAI) is an emerging discipline that merges spatial science, AI, ML and DL with HPC to extract knowledge for data-intensive geospatial problems [35].…”

Section: Integrating Intelligence Into Hpc-pmentioning

confidence: 99%

Exploring the Advancements in High-Performance Computing Paradigm for Remote Sensing Big Data Analytics

SK,

Aji S

2023

Cloud Computing and Data Science

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The incredible growth in Remote Sensing (RS) data volume, with high spectral-spatial-temporal resolutions, has been utilized in various application domains. With the rapid advancements in modern sensor technologies, including the 3D acquisition sensors, RS data with a large variety, velocity, veracity, varied value and incredible volume are generated, leading to the Remote Sensing Big Data (RSBD). With the high availability of RSBD, we require High-Performance Computing (HPC) environments for storing and processing these High-Dimensional (HD), complex, heterogeneous and distributed data. Also, introducing Deep Learning (DL) techniques in the RS domain demands more computing power, higher memory and networking bandwidth throughput capabilities, and optimized software and libraries to deliver the required performance. Motivated by this, we explore HPC computing environments for handling RSBD across multiple application domains in this paper. With particular emphasis on architectures such as cloud-based HPC, clusters, heterogeneous networks of computers, and specialized hardware architectures like Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs), we investigate how HPC technologies are being used to process RSBD efficiently while including integrated intelligence. This critical analysis results in a multi-layered cloud-based framework for efficient RSBD processing tasks. Also, we identified several data challenges to be handled while designing HPC frameworks. The findings from the study can help researchers better understand the HPC design concepts for developing RSBD frameworks.

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Section: Integrating Intelligence Into Hpc-pmentioning

confidence: 99%

Exploring the Advancements in High-Performance Computing Paradigm for Remote Sensing Big Data Analytics

SK,

Aji S

2023

Cloud Computing and Data Science

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“…As in other research fields, the requirement of rapid and effective solutions for processing the massive data associated to RS has led to the extended use of different computing paradigms during the last years. These include supercomputing, cloud computing, specialized hardware computing, and quantum computing, among others [31,32]. In particular, supercomputers have been widely used in RS applications to accelerate and scale the processes of image classification, target detection, clustering, registration, data fusion, compression or feature selection/extraction [33].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid CUDA, OpenMP, and MPI parallel TCA-based Domain Adaptation for Classification of Very High-Resolution Remote Sensing Images

Garea

Heras

Pedreira

et al. 2022

Preprint

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Domain Adaptation (DA) is a technique that aims at extracting information from a labeled remote sensing image to allow classifying a different image obtained by the same sensor but at a different geographical location. This is a very complex problem from the computational point of view, specially due to the very high-resolution multispectral images. TCANet is a deep learning neural network for DA classification problems that has been proven as very accurate for solving it. TCANet consists of several stages based on the application of convolutional filters obtained through Transfer Component Analysis (TCA) computed over the input images. It does not require training, in contrast to the usual CNN-based networks. In this paper, a hybrid parallel TCA-based domain adaptation technique for solving the classification of very high-resolution multispectral images is presented. It is designed for efficient execution on a multi-node computer by using message passing interface (MPI), exploiting the available Graphical Processing Units (GPUs), and making efficient use of each multicore node by using Open Multiprocessing (OpenMP). As a result, an accurate DA technique from the point of view of classification and with high speedup values over the sequential version is obtained, increasing the applicability of the technique to real problems.

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A hybrid CUDA, OpenMP, and MPI parallel TCA-based domain adaptation for classification of very high-resolution remote sensing images

et al. 2022

View full text Add to dashboard Cite

Domain Adaptation (DA) is a technique that aims at extracting information from a labeled remote sensing image to allow classifying a different image obtained by the same sensor but at a different geographical location. This is a very complex problem from the computational point of view, specially due to the very high-resolution of multispectral images. TCANet is a deep learning neural network for DA classification problems that has been proven as very accurate for solving them. TCANet consists of several stages based on the application of convolutional filters obtained through Transfer Component Analysis (TCA) computed over the input images. It does not require backpropagation training, in contrast to the usual CNN-based networks, as the convolutional filters are directly computed based on the TCA transform applied over the training samples. In this paper, a hybrid parallel TCA-based domain adaptation technique for solving the classification of very high-resolution multispectral images is presented. It is designed for efficient execution on a multi-node computer by using Message Passing Interface (MPI), exploiting the available Graphical Processing Units (GPUs), and making efficient use of each multicore node by using Open Multi-Processing (OpenMP). As a result, an accurate DA technique from the point of view of classification and with high speedup values over the sequential version is obtained, increasing the applicability of the technique to real problems.

show abstract

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

Cited by 6 publications

References 137 publications

Exploring the Advancements in High-Performance Computing Paradigm for Remote Sensing Big Data Analytics

Exploring the Advancements in High-Performance Computing Paradigm for Remote Sensing Big Data Analytics

A Hybrid CUDA, OpenMP, and MPI parallel TCA-based Domain Adaptation for Classification of Very High-Resolution Remote Sensing Images

A hybrid CUDA, OpenMP, and MPI parallel TCA-based domain adaptation for classification of very high-resolution remote sensing images

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