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PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing

Publication Type: 
Refereed Original Article
Abstract: 
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.
Digital Object Identifer (DOI): 
10.3390/rs12071092
Publication Status: 
Published
Date Accepted for Publication: 
Tuesday, 24 March, 2020
Publication Date: 
29/03/2020
Journal: 
Remote Sensing 2020
Volume: 
12
Issue: 
7
Pages: 
1092
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
Yes