Name
Abstract | ||
|---|---|---|
For multi-source image pixel-wise classification, each image information is different and complementary in the same area or scene. However, how to integrate them for decision-making is a difficult problem. In this paper, we focus on the characteristics of multi-source image and propose a novel pixel-wise classification method, named deep multi-level fusion network. The proposed method is to classify multi-sensor data including very high-resolution (VHR) RGB imagery, hyperspectral imagery (HSI) and multispectral light detection and ranging (MS-LiDAR) point cloud data. First, a deep spectral–spatial attention network is proposed to process HSI and MS-LiDAR images and get a learned classification map, which is based on feature level fusion. Next, a down-superpixel segmentation algorithm is proposed to get a segmentation result for VHR RGB imagery. Finally, the feature level fusion results are refinement by the down-superpixel segmentation results on the decision level, and get the final result. Extensive experiments and analyses on the data set grss_dfc_2018 demonstrate that the proposed multi-level fusion network can achieve a better result in the multi-source image pixel-wise classification. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.knosys.2021.106921 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Classification,Multi-level fusion,Attention,Segmentation | Journal | 221 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| X.L. Liu | 1 | 11 | 11.83 |
| Licheng Jiao | 2 | 5698 | 475.84 |
| Lingling Li | 3 | 1 | 1.70 |
| Xu Tang | 4 | 5 | 1.74 |
| Yuwei Guo | 5 | 0 | 0.34 |