Name
Title | ||
|---|---|---|
Deep Convolutional Highway Unit Network for SAR Target Classification With Limited Labeled Training Data. |
Abstract | ||
|---|---|---|
The deep convolutional neural network (CNN) has been widely used for target classification, because it can learn highly useful representations from data. However, it is difficult to apply a CNN for synthetic aperture radar (SAR) target classification directly, for it often requires a large volume of labeled training data, which is impractical for SAR applications. The highway network is a newly pr... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LGRS.2017.2698213 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Road transportation,Synthetic aperture radar,Feature extraction,Training,Training data,Transforms,Neural networks | Computer vision,Data set,Ensemble forecasting,Target acquisition,Convolutional neural network,Synthetic aperture radar,Feature extraction,Artificial intelligence,Deep learning,Artificial neural network,Mathematics | Journal |
Volume | Issue | ISSN |
14 | 7 | 1545-598X |
Citations | PageRank | References |
7 | 0.57 | 13 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Lin | 1 | 27 | 2.02 |
| Kefeng Ji | 2 | 176 | 17.01 |
| Miao Kang | 3 | 7 | 0.57 |
| Xiangguang Leng | 4 | 78 | 8.02 |
| Huanxin Zou | 5 | 184 | 19.43 |