Name
Title | ||
|---|---|---|
Evaluation of Time Series Distance Functions in the Task of Detecting Remote Phenology Patterns |
Abstract | ||
|---|---|---|
Phenology is the study of periodic natural phenomena and their relationship to climate. Usually, phenology studies consider the identification of patterns on temporal data. In those studies, several phenological change patterns are often encoded in time series for analysis and knowledge extraction. In this paper, we evaluate the effectiveness of several time series similarity functions in the task of classifying time series related to phonological phenomena characterized by near-surface vegetation indices extracted from images. In addition, we performed a correlation analysis to identify potential candidates for combination. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICPR.2014.539 | ICPR |
Keywords | Field | DocType |
geophysical techniques,remote sensing,vegetation,knowledge extraction,near-surface vegetation index,pattern identification,periodic natural phenomena,phenological change patterns,remote phenology pattern detection,temporal data,time series,time series distance function evaluation | Computer vision,Vegetation,Pattern recognition,Computer science,Temporal database,Knowledge extraction,Artificial intelligence,Change patterns,Correlation analysis,Phenology | Conference |
ISSN | Citations | PageRank |
1051-4651 | 3 | 0.40 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose C. Conti | 1 | 3 | 0.40 |
| Fabio A. Farial | 2 | 3 | 0.40 |
| Jurandy Almeida | 3 | 431 | 35.15 |
| Bruna Alberton | 4 | 62 | 7.50 |
| Leonor Patricia C. Morellato | 5 | 64 | 8.54 |
| Luiz Camolesi | 6 | 3 | 0.40 |
| Ricardo da Silva Torres | 7 | 787 | 61.46 |