Name
Abstract | ||
|---|---|---|
Classifying dense satellite image time series has become a necessity, especially with the recent efforts to create analysis ready data cubes. Approaches developed to perform this task are usually pixel-based. Even though these approaches can achieve good results, they do not take advantage of the intrinsic spatial correlation of geographic data nor do they consider spatial heterogeneity along with... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TGRS.2020.3033266 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
Image segmentation,Satellites,Time series analysis,Feature extraction,Spatiotemporal phenomena,Computational efficiency,Sensors | Journal | 59 |
Issue | ISSN | Citations |
9 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anderson Soares | 1 | 0 | 0.34 |
| Thales Sehn Korting | 2 | 24 | 12.47 |
| Leila Maria Garcia Fonseca | 3 | 47 | 17.89 |
| Hugo N. Bendini | 4 | 2 | 3.78 |