Name
Abstract | ||
|---|---|---|
Land cover classification aims at classifying each pixel in a satellite image into a particular land cover category, which can be regarded as a multi-class semantic segmentation task. In this paper, we propose a deep aggregation network for solving this task, which extracts and combines multi-layer features during the segmentation process. In particular, we introduce soft semantic labels and graph-based fine tuning in our proposed network for improving the segmentation performance. In our experiments, we demonstrate that our network performs favorably against state-of-the-art models on the dataset of DeepGlobe Satellite Challenge, while our ablation study further verifies the effectiveness of our proposed network architecture. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CVPRW.2018.00046 | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
Field | DocType | ISSN |
Pattern recognition,Computer science,Segmentation,Network architecture,Feature extraction,Image segmentation,Pixel,Artificial intelligence,Decoding methods,Land cover,Semantics | Conference | 2160-7508 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tzu-Sheng Kuo | 1 | 2 | 0.36 |
| Keng-Sen Tseng | 2 | 2 | 0.36 |
| Jia-Wei Yan | 3 | 2 | 1.03 |
| Yen-Cheng Liu | 4 | 48 | 7.12 |
| Yu-Chiang Frank Wang | 5 | 914 | 61.63 |