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Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification
| Content Provider | MDPI |
|---|---|
| Author | Xie, Wen Jiao, Licheng Hua, Wenqiang |
| Copyright Year | 2022 |
| Description | Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly, a stacked-dilated convolution layer with different dilation rates is constructed to capture multi-scale features of PolSAR image; meanwhile, the sharing weight is employed to reduce the calculation burden. Unfortunately, the labeled training samples of PolSAR image are usually limited. Then, the encoder–decoder structure of the original FCN is reconstructed with a U-net model. Finally, in view of the significance of the phase information for PolSAR images, the proposed model is trained in the complex-valued domain rather than the real-valued domain. The experiment results show that the classification performance of the proposed method is better than several state-of-the-art PolSAR image classification methods. |
| Starting Page | 3737 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14153737 |
| Journal | Remote Sensing |
| Issue Number | 15 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-04 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Imaging Science Fully Convolutional Network Stacked Dilated Convolution Complex-valued Domain Polsar Image Classificationn |
| Content Type | Text |
| Resource Type | Article |
