Name
Title | ||
|---|---|---|
DO-Net: Dual-Output Network for Land Cover Classification From Optical Remote Sensing Images |
Abstract | ||
|---|---|---|
Land cover classification is the basic task of remote sensing image interpretation. Related methods have developed rapidly, especially the branch based on deep learning (DL). For high-resolution remote sensing images, the smaller inter-class difference and greater intra-class difference are two obstacles to improving the classification accuracy. For the former, the DL models generally use a deeper encoder to extract more powerful classification features. Considering that the scale of different land cover categories varies greatly, multi-scale feature extraction modules are also used to improve the classification accuracy. While the latter is always overlooked, and thus we propose a dual-output model, which uses a dense spatial pyramid pooling (DSPP) module to generate both the pixel-level and region-level predictions, to reduce the influence of intra-class differences. To further increase the classification accuracy, we investigate the band selection technique to apply the pre-trained encoder from the natural red green blue (RGB) dataset to multi-spectral remote sensing images. Extensive experiments on two datasets demonstrate the effectiveness of our model. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3114305 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Feature extraction, Remote sensing, Training, Residual neural networks, Image resolution, Automobiles, Optical sensors, Deep learning (DL), dual-output, land cover classification, pre-trained model | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenchao Kang | 1 | 0 | 0.68 |
| Yuming Xiang | 2 | 15 | 6.30 |
| Feng Wang | 3 | 391 | 37.89 |
| Hongjian You | 4 | 103 | 17.44 |