Name
Title | ||
|---|---|---|
Support Vector Reduction in SVM Algorithm for Abrupt Change Detection in Remote Sensing |
Abstract | ||
|---|---|---|
Satellite imagery classification using the support vector machine (SVM) algorithm may be a time-consuming task. This may lead to unacceptable performances for risk management applications that are very time constrained. Hence, methods for accelerating the SVM classification are mandatory. From the SVM decision function, it can be noted that the classification time is proportional to the number of ... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/LGRS.2009.2020306 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Support vector machines,Change detection algorithms,Remote sensing,Support vector machine classification,Risk management,Classification algorithms,Hazards,Satellites,Detection algorithms,Image analysis | Structured support vector machine,Nonlinear system,Change detection,Remote sensing,Image processing,Artificial intelligence,Contextual image classification,Computer vision,Satellite imagery,Pattern recognition,Support vector machine,Algorithm,Statistical classification,Mathematics | Journal |
Volume | Issue | ISSN |
6 | 3 | 1545-598X |
Citations | PageRank | References |
3 | 0.49 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Habib | 1 | 3 | 0.49 |
| Jordi Inglada | 2 | 82 | 6.90 |
| G. Mercier | 3 | 3 | 0.49 |
| J. Chanussot | 4 | 306 | 18.20 |