| dc.contributor.author |
Petropoulos, GP |
en |
| dc.contributor.author |
Arvanitis, K |
en |
| dc.contributor.author |
Sigrimis, N |
en |
| dc.contributor.author |
Piromalis, DD |
en |
| dc.contributor.author |
Boglou, AK |
en |
| dc.date.accessioned |
2014-06-06T06:51:53Z |
|
| dc.date.available |
2014-06-06T06:51:53Z |
|
| dc.date.issued |
2012 |
en |
| dc.identifier.issn |
10823409 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1109/ICTAI.2012.184 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5757 |
|
| dc.subject |
Greece |
en |
| dc.subject |
Hyperion |
en |
| dc.subject |
Land cover mapping |
en |
| dc.subject |
Spectral Angle Mapper (SAM) |
en |
| dc.subject |
Support Vector Machines (SVMs) |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
Hyperion |
en |
| dc.subject.other |
Land cover mapping |
en |
| dc.subject.other |
Spectral angle mappers |
en |
| dc.subject.other |
Support vector machine (SVMs) |
en |
| dc.subject.other |
Artificial intelligence |
en |
| dc.subject.other |
Error statistics |
en |
| dc.subject.other |
Mapping |
en |
| dc.subject.other |
Maps |
en |
| dc.subject.other |
Remote sensing |
en |
| dc.subject.other |
Separation |
en |
| dc.subject.other |
Spectroscopy |
en |
| dc.subject.other |
Support vector machines |
en |
| dc.title |
Land use cartography from hyperion hyperspectral imagery analysis: Results from a mediterranean site |
en |
| heal.type |
conferenceItem |
en |
| heal.identifier.primary |
10.1109/ICTAI.2012.184 |
en |
| heal.identifier.secondary |
6495629 |
en |
| heal.publicationDate |
2012 |
en |
| heal.abstract |
Land cover is a fundamental variable of the Earth's system intimately connected with many parts of the human and physical environment. Recent advances in remote sensor technology have led to the launch of spaceborne hyperspectral remote sensing sensors, such as Hyperion. The present study is exploring the potential of Hyperion hyperspectral imagery combined with the Spectral Angle Mapper (SAM) and Support Vectors Machine (SVMs) pixel-based classifiers in obtaining land cover cartography. A typical Mediterranean setting was selected as a case study, located close to the capital of Greece. Validation of the derived thematic maps was performed on the basis of the error matrix statistics using for consistency the same set of validation points. Both classifiers produced generally reasonable results with the SVMs however significantly outperforming the SAM in both overall classification accuracy and kappa coefficient. The higher classification accuracy by SVMs was attributed principally to the classifier ability to identify an optimal separating hyperplane for classes' separation which allows a low generalization error, thus producing the best possible classes' separation. Yet, as a shortcoming of both classifiers was that none of them operates on a sub-pixel level, that potentially reduces their accuracy as a result of spectral mixing problems that can be commonly found in coarse spatial resolution imagery and at fragmented landscapes. © 2012 IEEE. |
en |
| heal.journalName |
Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI |
en |
| dc.identifier.volume |
2 |
en |
| dc.identifier.doi |
10.1109/ICTAI.2012.184 |
en |
| dc.identifier.spage |
26 |
en |
| dc.identifier.epage |
31 |
en |