Name
Abstract | ||
|---|---|---|
An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification. The proposed method is comprised of a feature extraction module, an attention module, and a fusion module. The attention module is a plug-and-play linear self-attention module that can be extensively used in any model. The proposed model has achieved the overall accuracy of 95.40\% on the Houston dataset. The experimental results demonstrate the superiority of the proposed method over other state-of-the-art models. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IGARSS47720.2021.9553769 | IGARSS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Feng | 1 | 10 | 3.68 |
| Feng Gao | 2 | 562 | 55.55 |
| Jian Fang | 3 | 4 | 2.48 |
| Junyu Dong | 4 | 0 | 2.03 |