Potential evaluation of different types of images and their combination for the classification of gis objects cropland and grassland

Show simple item record

dc.identifier.uri http://dx.doi.org/10.15488/1109
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1133
dc.contributor.author Recio, J. A.
dc.contributor.author Helmholz, Petra
dc.contributor.author Müller, S.
dc.date.accessioned 2017-02-03T08:18:42Z
dc.date.available 2017-02-03T08:18:42Z
dc.date.issued 2011
dc.identifier.citation Recio, J. A.; Helmholz, P.; Mueller, S.: Potential evaluation of different types of images and their combination for the classification of gis objects cropland and grassland. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [ISPRS Hannover Workshop 2011: High-Resolution Earth Imaging For Geospatial Information] 38-4 (2011), Nr. W19, S. 251-257. DOI: https://doi.org/10.5194/isprsarchives-XXXVIII-4-W19-251-2011
dc.description.abstract In many publications the performance of different classification algorithms regarding to agricultural classes is evaluated. In contrast, this paper focuses on the potential of different imagery for the classification of the two most frequent classes: cropland and grassland. For our experiments three categories of imagery, high resolution aerial images, high resolution RapidEye satellite images and medium resolution Disaster Monitoring Constellation (DMC) satellite images are examined. An object-based image classification, as one of the most reliable methods for the automatic updating and evaluation of landuse geospatial databases, is chosen. The object boundaries are taken from a GIS database, each object is described by means of a set of image based features. Spectral, textural and structural (semivariogram derived) features are extracted from images of different dates and sensors. During classification a supervised decision trees generating algorithm is applied. To evaluate the potential of the different images, all possible combinations of the available image data are tested during classification. The results show that the best performance of landuse classification is based on RapidEye data (overall accuracy of 90%), obtaining slightly accuracy increases when this imagery is combined with additional image data (overall accuracy of 92%). eng
dc.description.sponsorship BKG
dc.description.sponsorship Polytechnic University of Valencia
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof ISPRS Hannover Workshop on High-Resolution Earth Imaging for Geospatial Information, June 14-17, 2011, Hannover, Germany
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences: [ISPRS Hannover Workshop 2011: High-Resolution Earth Imaging For Geospatial Information] 38-4 (2011), Nr. W19
dc.rights CC BY 3.0
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject Agriculture eng
dc.subject Classification eng
dc.subject Multitemporal eng
dc.subject Aerial eng
dc.subject Satellite eng
dc.subject Orthoimage eng
dc.subject Resolution eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Potential evaluation of different types of images and their combination for the classification of gis objects cropland and grassland
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 2194-9034
dc.relation.doi https://doi.org/10.5194/isprsarchives-XXXVIII-4-W19-251-2011
dc.bibliographicCitation.issue W19
dc.bibliographicCitation.volume 38-4
dc.bibliographicCitation.firstPage 251
dc.bibliographicCitation.lastPage 257
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s):

Show simple item record

 

Search the repository


Browse

My Account

Usage Statistics