Name
Abstract | ||
|---|---|---|
Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/rs11232862 | REMOTE SENSING |
Keywords | DocType | Volume |
Polarimetric synthetic aperture radar (PolSAR),ship detection,deep convolutional neural network (DCNN) | Journal | 11 |
Issue | Citations | PageRank |
23 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Fan | 1 | 53 | 7.96 |
| Feng Zhou | 2 | 2189 | 158.01 |
| Xueru Bai | 3 | 169 | 25.80 |
| Mingliang Tao | 4 | 68 | 10.49 |
| Tian Tian | 5 | 0 | 0.68 |