Name
Title | ||
|---|---|---|
A Unified Multimodal Deep Learning Framework For Remote Sensing Imagery Classification |
Abstract | ||
|---|---|---|
In this paper, we present a unified deep learning framework for multimodal remote sensing image classification, U-MDL for short. U-MDL attempts to develop a general network architecture that consists of two subnetworks for feature extraction and feature fusion, respectively, with a focus on "which", "when", and "how" to fuse. For this purpose, we detail several common but effective fusion modules ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WHISPERS52202.2021.9484057 | 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | DocType | ISBN |
Deep learning,Fuses,Conferences,Network architecture,Feature extraction,Reproducibility of results,Task analysis | Conference | 978-1-6654-3601-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danfeng Hong | 1 | 183 | 33.29 |
| Lianru Gao | 2 | 373 | 59.90 |
| Xin Wu | 3 | 16 | 3.89 |
| Jing Yao | 4 | 0 | 0.34 |
| Naoto Yokoya | 5 | 439 | 36.36 |
| Bing Zhang | 6 | 0 | 0.34 |