Name
Abstract | ||
|---|---|---|
Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a 3-D cube. To handle this type of remote sensing data, on the one side, unidimensional dynamical models have been considered, modeling each pixel separately along the time (pixel-based approach), and exploring the temporal correlation. On the other side, 2-D approaches have been considered to process each image at one date, exploring the spatial correlation. In this article, we propose a new 3-D autoregressive (AR) (3-D-AR) model useful for multitemporal image interpretation exploring the correlation in three dimensions altogether. The 3-D-AR model is statistically defined, and a robust parameter estimation method is discussed. The tools for filtering, forecasting, and detecting anomalies are also introduced. A Monte Carlo simulation study is performed to evaluate the finite signal length performance of the robust estimation and its sensitivity to outliers. The proposed model is applied to a multitemporal normalized difference vegetation index (NDVI) image stack for filtering, prediction, and anomaly detection purposes. The numerical results show the importance of the proposed 3-D-AR model for spatiotemporal remote sensing data interpretation. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TGRS.2020.2998295 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | DocType | Volume |
3-D model,anomaly detection,filtering,remote sensing,robust estimation,spatiotemporal data | Journal | 59 |
Issue | ISSN | Citations |
2 | 0196-2892 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Debora M. Bayer | 1 | 0 | 0.34 |
| Fábio M. Bayer | 2 | 126 | 12.89 |
| Paolo Gamba | 3 | 682 | 92.97 |