Name
Abstract | ||
|---|---|---|
We consider synthetic aperture radar (SAR) image recovery and classification from sub-Nyquist samples, i.e., compressive SAR. Our approach is to first apply back-projection and then use a deep convolutional neural network (CNN) to dealias the result. Importantly, our CNN is trained to be agnostic to the subsampling pattern. Relative to the basis pursuit (i.e., sparsity-based) approach to compressive SAR recovery, our CNN-based approach is faster and more accurate, in terms of both image recovery MSE and downstream classification accuray, on the MSTAR dataset. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IEEECONF44664.2019.9049022 | CONFERENCE RECORD OF THE 2019 FIFTY-THIRD ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS |
DocType | ISSN | Citations |
Conference | 1058-6393 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Wharton | 1 | 0 | 0.34 |
| Edward T. Reehorst | 2 | 0 | 0.34 |
| Philip Schniter | 3 | 1620 | 93.74 |