Name
Title | ||
|---|---|---|
Evolutionary approach to discovery of classification rules from remote sensing images |
Abstract | ||
|---|---|---|
In this article a new method for classification of remote sensing images is described. For most applications, these images contain voluminous, complex, and sometimes noisy data. For the approach presented herein, image classification rules are discovered by an evolution-based process, rather than by applying an a priori chosen classification algorithm. During the evolution process, classification rules are created using raw remote sensing images, the expertise encoded in classified zones of images, and statistics about related thematic objects. The resultant set of evolved classification rules are simple to interpret, efficient, robust and noise resistant. This evolution-based approach is detailed and validated based on remote sensing images covering not only urban zones of Strasbourg, France, but also vegetation zones of the lagoon of Venice. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-36605-9_36 | EvoWorkshops |
Keywords | Field | DocType |
image classification | Data mining,Noisy data,Classification rule,Evolutionary algorithm,Computer science,Remote sensing,A priori and a posteriori,Signal-to-noise ratio,Thematic map,Contextual image classification,Genetic algorithm | Conference |
Volume | ISSN | ISBN |
2611 | 0302-9743 | 3-540-00976-0 |
Citations | PageRank | References |
1 | 0.40 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jerzy Korczak | 1 | 62 | 18.72 |
| Arnaud Quirin | 2 | 168 | 13.68 |