dc.identifier.uri |
http://dx.doi.org/10.15488/1064 |
|
dc.identifier.uri |
http://www.repo.uni-hannover.de/handle/123456789/1088 |
|
dc.contributor.author |
Nagabhatla, Nidhi
|
|
dc.contributor.author |
Kühle, Peter
|
|
dc.date.accessioned |
2017-01-27T08:36:51Z |
|
dc.date.available |
2017-01-27T08:36:51Z |
|
dc.date.issued |
2016 |
|
dc.identifier.citation |
Nagabhatla, N.; Kühle, P.: Tropical agrarian landscape classification using high-resolution GeoEYE data and segmentationbased approach. In: European Journal of Remote Sensing 49 (2016), S. 623-642. DOI: https://doi.org/10.5721/EuJRS20164933 |
|
dc.description.abstract |
We examine the use of high spatial resolution ‘GeoEYE’ imagery for land use classification in a tropical landscape. Image objects (I-Os) derived from features identification provide a basis for segmentation process and the Geographic Object Based Image Analysis (GEOBIA) framework. eCognition software with I-Os as classification unit and maximum likelihood algorithm facilitated the process. Supervised classification approaches (SCA) and rule set classification approach (RSCA) were used and performance and transferability of two approaches compared. Main conclusions: (a) high degree of details in GeoEYE data enables delineation of diverse land use zones, and (b) segmentation based analysis is more effective to tackle spatial intermixing. © 2016 by the authors. |
eng |
dc.description.sponsorship |
BMBF |
|
dc.language.iso |
eng |
|
dc.publisher |
Firenze : Associazione Italiana di Telerilevamento |
|
dc.relation.ispartofseries |
European Journal of Remote Sensing 49 (2016) |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
GeoEYE |
eng |
dc.subject |
High-resolution |
eng |
dc.subject |
Image objects (I-Os) |
eng |
dc.subject |
Land use assessment |
eng |
dc.subject |
Segmentation |
eng |
dc.subject |
Tropical |
eng |
dc.subject |
Land use |
eng |
dc.subject |
Maximum likelihood |
eng |
dc.subject |
Osmium |
eng |
dc.subject |
GeoEYE |
eng |
dc.subject |
Geographic object-based image analysis |
eng |
dc.subject |
High resolution |
eng |
dc.subject |
Image objects |
eng |
dc.subject |
Landuse classifications |
eng |
dc.subject |
Maximum likelihood algorithm |
eng |
dc.subject |
Supervised classification |
eng |
dc.subject |
Tropical |
eng |
dc.subject |
Image segmentation |
eng |
dc.subject.ddc |
550 | Geowissenschaften
|
ger |
dc.title |
Tropical Agrarian landscape classification using high-resolution GeoEYE data and segmentationbased approach |
eng |
dc.type |
Article |
|
dc.type |
Text |
|
dc.relation.essn |
2279-7254 |
|
dc.relation.issn |
1129-8596 |
|
dc.relation.doi |
https://doi.org/10.5721/EuJRS20164933 |
|
dc.bibliographicCitation.volume |
49 |
|
dc.bibliographicCitation.firstPage |
623 |
|
dc.bibliographicCitation.lastPage |
642 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|