| Autor: |
Shuangmei Zhao, Haitao Lang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 8038-8048 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2022.3206753 |
| Popis: |
This study aims at improving fine-grained ship classification performance under the condition that there is no labeled samples available in SAR domain (target domain) by transferring the knowledge from optical remote sensing (ORS) domain (source domain), which has rich labeled samples. The proposed method improves the original deep subdomain adaptation network (DSAN) by designing a dual-branch network (DBN) embedding attention module to extract more discriminative deep transferable features, thereby improving the performance of the subdomain adaptation. Specifically, we utilized a deep base network (ResNet-50) and a shallow base network (ResNet-18) to build the DBN, and embedded the convolutional block attention module after the first and the last convolutional layer of each branch. Extensive experiments demonstrate that the proposed method, which is termed as DSAN++, is feasible and achieves remarkable improvement than the state-of-the-art methods on the task of fine-grained ship classification. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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