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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Gueguen, L. Hamid, R. |
| Copyright Year | 2015 |
| Description | Author affiliation: DigitalGlobe Inc., Thornton, CO, USA (Gueguen, L.; Hamid, R.) |
| Abstract | Satellite imagery is a valuable source of information for assessing damages in distressed areas undergoing a calamity, such as an earthquake or an armed conflict. However, the sheer amount of data required to be inspected for this assessment makes it impractical to do it manually. To address this problem, we present a semi-supervised learning framework for large-scale damage detection in satellite imagery. We present a comparative evaluation of our framework using over 88 million images collected from 4, 665 $KM^{2}$ from 12 different locations around the world. To enable accurate and efficient damage detection, we introduce a novel use of hierarchical shape features in the bags-of-visual words setting. We analyze how practical factors such as sun, sensor-resolution, satellite-angle, and registration differences impact the effectiveness our proposed representation, and compare it to five alternative features in multiple learning settings. Finally, we demonstrate through a user-study that our semi-supervised framework results in a ten-fold reduction in human annotation time at a minimal loss in detection accuracy compared to manual inspection. |
| Starting Page | 1321 |
| Ending Page | 1328 |
| File Size | 1086648 |
| Page Count | 8 |
| File Format | |
| ISSN | 10636919 |
| e-ISBN | 9781467369640 |
| DOI | 10.1109/CVPR.2015.7298737 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-06-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Shape Satellites Feature extraction Sun Image resolution Strips Level set |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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