Name
Abstract | ||
|---|---|---|
Change detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-319-02895-8_40 | ACIVS |
Keywords | Field | DocType |
database,image,change detection,classification,updating | Geospatial analysis,Data mining,False detection,Satellite,Change detection,Pattern recognition,Computer science,Artificial intelligence,Decision process,Limiting | Conference |
Volume | ISSN | Citations |
8192 | 0302-9743 | 3 |
PageRank | References | Authors |
0.61 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrien Gressin | 1 | 8 | 3.03 |
| Nicole Vincent | 2 | 218 | 26.66 |
| Clément Mallet | 3 | 134 | 15.93 |
| N. Paparoditis | 4 | 99 | 18.52 |