Abstract
We test this premise and explore representation spaces from a single deep convolutional network and their visualization to argue for a novel unified feature extraction framework. The objective is to utilize and re-purpose trained feature extractors without the need for network retraining on three remote sensing tasks i.e. superpixel mapping, pixel-level segmentation and semantic based image visualization. By leveraging the same convolutional feature extractors and viewing them as visual information extractors that encode different settlement representation spaces, we demonstrate a preliminary inductive transfer learning potential on multiscale experiments that incorporate edge-level details up to semantic-level information.
Original language | English |
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Title of host publication | 2017 IEEE International Geoscience and Remote Sensing Symposium |
Subtitle of host publication | International Cooperation for Global Awareness, IGARSS 2017 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 3779-3782 |
Number of pages | 4 |
ISBN (Electronic) | 9781509049516 |
DOIs | |
State | Published - Dec 1 2017 |
Event | 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 - Fort Worth, United States Duration: Jul 23 2017 → Jul 28 2017 |
Publication series
Name | International Geoscience and Remote Sensing Symposium (IGARSS) |
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Volume | 2017-July |
Conference
Conference | 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017 |
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Country/Territory | United States |
City | Fort Worth |
Period | 07/23/17 → 07/28/17 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.
Keywords
- Convolutional neural networks
- Inductive transfer learning
- Representation learning
- Segmentation
- Settlement mapping