Name
Title | ||
|---|---|---|
Iterative Deep Learning (IDL) for agricultural landscape classification using fine spatial resolution remotely sensed imagery |
Abstract | ||
|---|---|---|
•A novel Iterative Deep Learning (IDL) was proposed for crop classification.•IDL models relationship between low-level crop (LLC) and high-level crop (HLC).•LLC classification and HLC classification refine each other through interaction.•IDL consistently achieved the greatest accuracy in comparison to benchmarks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.jag.2021.102437 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | DocType | Volume |
Image classification,Hierarchical crop classification,Iterative deep learning,Object-based image analysis (OBIA),Convolutional neural network (CNN) | Journal | 102 |
ISSN | Citations | PageRank |
1569-8432 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huapeng Li | 1 | 3 | 1.72 |
| Ce Zhang | 2 | 0 | 0.68 |
| Shuqing Zhang | 3 | 1 | 0.70 |
| Xiaohui Ding | 4 | 0 | 0.34 |
| Peter M. Atkinson | 5 | 0 | 0.34 |