Supervised learning on graphs of spatio-temporal similarity in satellite image sequences
| dc.creator | Héas, Patrick | |
| dc.creator | Datcu, Mihai | |
| dc.date | 2007-09-19 | |
| dc.date | 2007-09-20 | |
| dc.date.accessioned | 2026-07-07T08:30:51Z | |
| dc.date.available | 2026-07-07T08:30:51Z | |
| dc.description | High resolution satellite image sequences are multidimensional signals composed of spatio-temporal patterns associated to numerous and various phenomena. Bayesian methods have been previously proposed in (Heas and Datcu, 2005) to code the information contained in satellite image sequences in a graph representation using Bayesian methods. Based on such a representation, this paper further presents a supervised learning methodology of semantics associated to spatio-temporal patterns occurring in satellite image sequences. It enables the recognition and the probabilistic retrieval of similar events. Indeed, graphs are attached to statistical models for spatio-temporal processes, which at their turn describe physical changes in the observed scene. Therefore, we adjust a parametric model evaluating similarity types between graph patterns in order to represent user-specific semantics attached to spatio-temporal phenomena. The learning step is performed by the incremental definition of similarity types via user-provided spatio-temporal pattern examples attached to positive or/and negative semantics. From these examples, probabilities are inferred using a Bayesian network and a Dirichlet model. This enables to links user interest to a specific similarity model between graph patterns. According to the current state of learning, semantic posterior probabilities are updated for all possible graph patterns so that similar spatio-temporal phenomena can be recognized and retrieved from the image sequence. Few experiments performed on a multi-spectral SPOT image sequence illustrate the proposed spatio-temporal recognition method. | |
| dc.identifier | https://arxiv.org/abs/0709.3013 | |
| dc.identifier | http://arxiv.org/abs/0709.3013 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/138348 | |
| dc.subject | Computer Vision and Pattern Recognition | |
| dc.title | Supervised learning on graphs of spatio-temporal similarity in satellite image sequences | |
| dc.type | text |