Name
Abstract | ||
|---|---|---|
In the training of deep learning models, how the model parameters are initialized greatly affects the model performance, sample efficiency, and convergence speed. Recently, representation learning for model initialization has been actively studied in the remote sensing field. In particular, the appearance characteristics of the imagery obtained using the synthetic aperture radar (SAR) sensor are quite different from those of general electro-optical (EO) images, and thus, representation learning is even more important in remote sensing domain. Motivated from contrastive multiview coding, we propose multimodal representation learning for SAR semantic segmentation. Unlike previous studies, our method jointly uses EO imagery, SAR imagery, and a label mask. Several experiments show that our approach is superior to the existing methods in model performance, sample efficiency, and convergence speed. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3109345 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Image segmentation, Synthetic aperture radar, Training, Radar polarimetry, Semantics, Encoding, Sensors, Contrastive multiview coding, data fusion, multimodal representation learning, synthetic aperture radar (SAR) semantic segmentation | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Keumgang Cha | 1 | 0 | 0.34 |
| Junghoon Seo | 2 | 0 | 1.01 |
| Yeji Choi | 3 | 0 | 3.72 |