Add to ORKGSimilarly to other scientific domains, Deep Learning (DL) holds great promises to fulfil the challenging needs of Remote Sensing (RS) applications. However, the increase in volume, variety and complexity of acquisitions that are carried out on a daily basis by Earth Observation (EO) missions generates new processing and storage challenges within operational processing pipelines. The aim of this work is to show that High-Performance Computing (HPC) systems can speed up the training time of Convolutional Neural Networks (CNNs). Particular attention is put on the monitoring of the classification accuracy that usually degrades when using large batch sizes. The experimental results of this work show that the training of the model scales up to a ...
International audienceWe propose a convolutional neural network (CNN) model for remote sensing image...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution im...
Numerous convolution neural networks increase accuracy of classification for remote sensing scene im...
High-Performance Computing (HPC) has recently been attracting more attention in remote sensing appli...
A wide variety of Remote Sensing (RS) missions arecontinuously acquiring a large volume of data ever...
Deep Learning models have proven necessary in dealing with the challenges posed by the continuous gr...
Land-cover classification methods are based on the processing of large image volumes to accurately e...
Convolutional neural networks (CNN) are revolutionizing and improving today\u27s technological lands...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate class...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Numerous convolution neural networks increase accuracy of classification for remote sensing scene im...
Convolutional neural networks (CNNs) have proven to be very efficient for the analysis of remote sen...
International audienceWe propose a convolutional neural network (CNN) model for remote sensing image...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution im...
Numerous convolution neural networks increase accuracy of classification for remote sensing scene im...
High-Performance Computing (HPC) has recently been attracting more attention in remote sensing appli...
A wide variety of Remote Sensing (RS) missions arecontinuously acquiring a large volume of data ever...
Deep Learning models have proven necessary in dealing with the challenges posed by the continuous gr...
Land-cover classification methods are based on the processing of large image volumes to accurately e...
Convolutional neural networks (CNN) are revolutionizing and improving today\u27s technological lands...
In using the convolutional neural network (CNN) for classification, there is a set of hyperparameter...
Deep learning is a branch of machine learning that aims to extract multiple simple features from da...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate class...
Convolutional deep neural networks (CNNs) has been shown to perform well in difficult learning tasks...
Numerous convolution neural networks increase accuracy of classification for remote sensing scene im...
Convolutional neural networks (CNNs) have proven to be very efficient for the analysis of remote sen...
International audienceWe propose a convolutional neural network (CNN) model for remote sensing image...
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution im...
Numerous convolution neural networks increase accuracy of classification for remote sensing scene im...