Name
Abstract | ||
|---|---|---|
We present an assessment of stepwise co-registration procedures applied to multi-temporal SAR datasets. Images are connected in pairs through a minimum spanning tree structure, obtained by adopting a distance measure which is a function of the expected co-registration quality. Experiments have been performed on a test dataset by a) directly estimating the (a posteriori) co-registration quality over all possible image combinations, b) using an a priori model inspired by similar models for the multi-temporal InSAR coherence, with parameters obtained experimentally, c) using the same a priori model with first-guess parameters. Performances were evaluated by analyzing the amplitude inverse coefficient of variation distribution over the co-registered image stacks obtained by the three procedures above. Results show, that, although the best coupling strategy depends on the particular dataset and is thus difficult to model via general rules, a non-negligible improvement in the performance of Persistent Scatterers Interferometry techniques can he obtained by adopting stepwise, approaches based on a priori models for the expected co-registration quality, rather than using a single acquisition as master. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/IGARSS.2004.1369970 | IGARSS |
Keywords | Field | DocType |
data acquisition,geophysical signal processing,image registration,multidimensional signal processing,radiowave interferometry,remote sensing by radar,synthetic aperture radar,tree data structures,coregistered image stacks,distance measure,first-guess parameters,image registration,minimum spanning tree structure,multitemporal DInSAR,multitemporal InSAR coherence,multitemporal SAR datasets,persistent scatterers interferometry,stepwise coregistration procedures,stepwise processing,synthetic aperture radar,variation distribution | Computer vision,Multidimensional signal processing,Radar imaging,Computer science,Synthetic aperture radar,Tree (data structure),Remote sensing,A priori and a posteriori,Data acquisition,Artificial intelligence,Image registration,Minimum spanning tree | Conference |
Volume | ISSN | Citations |
6 | 2153-6996 | 1 |
PageRank | References | Authors |
0.38 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Refice | 1 | 46 | 12.80 |
| Fabio Bovenga | 2 | 70 | 19.41 |
| Raffaele Nutricato | 3 | 37 | 12.25 |
| Maria Teresa Chiaradia | 4 | 9 | 7.69 |