Details
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 204-218 |
Seitenumfang | 15 |
Fachzeitschrift | ISPRS Journal of Photogrammetry and Remote Sensing |
Jahrgang | 97 |
Frühes Online-Datum | 8 Okt. 2014 |
Publikationsstatus | Veröffentlicht - 1 Nov. 2014 |
Abstract
Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Atom- und Molekularphysik sowie Optik
- Ingenieurwesen (insg.)
- Ingenieurwesen (sonstige)
- Informatik (insg.)
- Angewandte Informatik
- Erdkunde und Planetologie (insg.)
- Computer in den Geowissenschaften
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in: ISPRS Journal of Photogrammetry and Remote Sensing, Jahrgang 97, 01.11.2014, S. 204-218.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Semi-automatic verification of cropland and grassland using very high resolution mono-temporal satellite images
AU - Helmholz, Petra
AU - Rottensteiner, Franz
AU - Heipke, Christian
N1 - Funding Information: The work was carried out as part of the WiPKA project ( Helmholz et al., 2012 ). The project was supported by the German Federal Agency for Cartography and Geodesy (BKG). The support is gratefully acknowledged.
PY - 2014/11/1
Y1 - 2014/11/1
N2 - Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.
AB - Many public and private decisions rely on geospatial information stored in a GIS database. For good decision making this information has to be complete, consistent, accurate and up-to-date. In this paper we introduce a new approach for the semi-automatic verification of a specific part of the, possibly outdated GIS database, namely cropland and grassland objects, using mono-temporal very high resolution (VHR) multispectral satellite images. The approach consists of two steps: first, a supervised pixel-based classification based on a Markov Random Field is employed to extract image regions which contain agricultural areas (without distinction between cropland and grassland), and these regions are intersected with boundaries of the agricultural objects from the GIS database. Subsequently, GIS objects labelled as cropland or grassland in the database and showing agricultural areas in the image are subdivided into different homogeneous regions by means of image segmentation, followed by a classification of these segments into either cropland or grassland using a Support Vector Machine. The classification result of all segments belonging to one GIS object are finally merged and compared with the GIS database label. The developed approach was tested on a number of images. The evaluation shows that errors in the GIS database can be significantly reduced while also speeding up the whole verification task when compared to a manual process.
KW - Automation
KW - GIS
KW - Mono-temporal
KW - Quality control
KW - Satellite images
KW - Verification
UR - http://www.scopus.com/inward/record.url?scp=84907756097&partnerID=8YFLogxK
U2 - 10.1016/j.isprsjprs.2014.09.008
DO - 10.1016/j.isprsjprs.2014.09.008
M3 - Article
AN - SCOPUS:84907756097
VL - 97
SP - 204
EP - 218
JO - ISPRS Journal of Photogrammetry and Remote Sensing
JF - ISPRS Journal of Photogrammetry and Remote Sensing
SN - 0924-2716
ER -