PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing
Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Anothe...
Main Authors: | , , |
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Format: | Article |
Language: | English |
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MDPI AG
2020-03-01
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Series: | Remote Sensing |
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Online Access: | https://www.mdpi.com/2072-4292/12/7/1092 |
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author | David Browne Michael Giering Steven Prestwich |
author_facet | David Browne Michael Giering Steven Prestwich |
author_sort | David Browne |
collection | DOAJ |
description | Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets. |
first_indexed | 2024-03-11T10:09:59Z |
format | Article |
id | doaj.art-87edacbb49d543c780225b985308f48c |
institution | Directory Open Access Journal |
issn | 2072-4292 |
language | English |
last_indexed | 2024-03-11T10:09:59Z |
publishDate | 2020-03-01 |
publisher | MDPI AG |
record_format | Article |
series | Remote Sensing |
spelling | doaj.art-87edacbb49d543c780225b985308f48c2023-11-16T14:34:33ZengMDPI AGRemote Sensing2072-42922020-03-01127109210.3390/rs12071092PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote SensingDavid Browne0Michael Giering1Steven Prestwich2Insight Centre for Data Analytics, University College Cork, Cork City T12 XF62, IrelandUnited Technologies Research Centre, Penrose Wharf Business Centre, Penrose Quay, Cork City T23 XN53, IrelandInsight Centre for Data Analytics, University College Cork, Cork City T12 XF62, IrelandScene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learning approach to tackle these problems. We use transfer learning to compensate for the lack of data, and data augmentation to tackle varying scale and orientation. To reduce network size, we use a novel unsupervised learning approach based on k-means clustering, applied to all parts of the network: most network reduction methods use computationally expensive supervised learning methods, and apply only to the convolutional or fully connected layers, but not both. In experiments, we set new standards in classification accuracy on four remote-sensing and two scene-recognition image datasets.https://www.mdpi.com/2072-4292/12/7/1092pruning networksnetwork compressionremote-sensing image classificationtransfer learningConvolutional Neural Networkpre-trained AlexNet |
spellingShingle | David Browne Michael Giering Steven Prestwich PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing Remote Sensing pruning networks network compression remote-sensing image classification transfer learning Convolutional Neural Network pre-trained AlexNet |
title | PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing |
title_full | PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing |
title_fullStr | PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing |
title_full_unstemmed | PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing |
title_short | PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing |
title_sort | pulsenetone fast unsupervised pruning of convolutional neural networks for remote sensing |
topic | pruning networks network compression remote-sensing image classification transfer learning Convolutional Neural Network pre-trained AlexNet |
url | https://www.mdpi.com/2072-4292/12/7/1092 |
work_keys_str_mv | AT davidbrowne pulsenetonefastunsupervisedpruningofconvolutionalneuralnetworksforremotesensing AT michaelgiering pulsenetonefastunsupervisedpruningofconvolutionalneuralnetworksforremotesensing AT stevenprestwich pulsenetonefastunsupervisedpruningofconvolutionalneuralnetworksforremotesensing |