02/10/2020 - A Three-dimensional Spatiotemporal Model for Remote Sensing Data Cubes - Speaker: Fábio Mariano Bayer (UFSM)

UFSCar/USP joint Seminar

Schudele
02/10/2020 to 14h

Localization:
Google Meet - Presentation link (up to one hour before the lecture at www.pipges.ufscar.br)

Title:
A Three-dimensional Spatiotemporal Model for Remote Sensing Data Cubes

Speaker:
Fábio Mariano Bayer (UFSM)

Abstract:
Satellite images from the same scene observed over time can be composed in an image stack, which could be modeled as a three-dimensional (3D) 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), exploring the temporal correlation. On the other side, two-dimensional approaches have been considered to process each image at one date, exploring the spatial correlation. In this paper, we propose a new 3D autoregressive (3D-AR) model useful for multitemporal image interpretation exploring the correlation in three dimensions altogether. The 3D-AR model is statistically defined and a robust parameter estimation method is discussed. 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 3D-AR model for spatiotemporal remote sensing data interpretation.