| dc.contributor.author |
Petropoulos, GP |
en |
| dc.contributor.author |
Arvanitis, K |
en |
| dc.contributor.author |
Sigrimis, N |
en |
| dc.date.accessioned |
2014-06-06T06:51:50Z |
|
| dc.date.available |
2014-06-06T06:51:50Z |
|
| dc.date.issued |
2012 |
en |
| dc.identifier.issn |
09574174 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.eswa.2011.09.083 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5729 |
|
| dc.subject |
Artificial Neural Networks (ANNs) |
en |
| dc.subject |
Classification |
en |
| dc.subject |
Greece |
en |
| dc.subject |
Hyperion |
en |
| dc.subject |
Land cover/use mapping |
en |
| dc.subject |
Mediterranean |
en |
| dc.subject |
Remote sensing |
en |
| dc.subject |
Support Vector Machines (SVMs) |
en |
| dc.subject.other |
Greece |
en |
| dc.subject.other |
Hyperion |
en |
| dc.subject.other |
Land cover/use |
en |
| dc.subject.other |
Mediterranean |
en |
| dc.subject.other |
Support vector |
en |
| dc.subject.other |
Climatology |
en |
| dc.subject.other |
Data reduction |
en |
| dc.subject.other |
Image resolution |
en |
| dc.subject.other |
Landforms |
en |
| dc.subject.other |
Maps |
en |
| dc.subject.other |
Neural networks |
en |
| dc.subject.other |
Spatial distribution |
en |
| dc.subject.other |
Support vector machines |
en |
| dc.subject.other |
Weathering |
en |
| dc.subject.other |
Remote sensing |
en |
| dc.title |
Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1016/j.eswa.2011.09.083 |
en |
| heal.publicationDate |
2012 |
en |
| heal.abstract |
Describing the pattern and the spatial distribution of land cover is traditionally based on remote sensing data analysis and one of the most commonly techniques applied has been image classification. The main objective of the present study has been to evaluate the combined use of Hyperion hyperspectral imagery with the Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) classifiers for discriminating land-cover classes in a typical Mediterranean setting. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classification map, using for consistency the same set of validation points. Results indicated a close classification accuracy between the two classifiers, with the SVMs somehow outperforming the ANNs by 3.31% overall accuracy and by 0.038 kappa coefficient. Although both classifiers produced close results, SVMs generally appeared most useful in describing the spatial distribution and the cover density of each land cover category. The higher classification accuracy by SVMs was attributed principally to the ability of this classifier to identify an optimal separating hyperplane for classes' separation which allows a low generalization error, thus producing the best possible classes' separation. On the other, as a key disadvantage of both techniques was identified that both do not operate on a sub-pixel level, which can significantly reduce their accuracy due to possible mixture problems occurred when coarse spatial resolution remote sensing imagery is used. All in all, this study demonstrated that, provided that a Hyperion hyperspectral imagery can be made available at regular time intervals over a given region, when combined with either SVMs or ANNs classifiers, can potentially enable a wider approach in land use/cover mapping. This can be of particular importance, especially for regions like in the Mediterranean basin, since it can be related to mapping and monitoring of land degradation and desertification phenomena which are evident in such areas. © 2011 Elsevier Ltd. All rights reserved. |
en |
| heal.journalName |
Expert Systems with Applications |
en |
| dc.identifier.issue |
3 |
en |
| dc.identifier.volume |
39 |
en |
| dc.identifier.doi |
10.1016/j.eswa.2011.09.083 |
en |
| dc.identifier.spage |
3800 |
en |
| dc.identifier.epage |
3809 |
en |