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

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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

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1109

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Sum total of downloads: 89




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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%).
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2011
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 71 79.78%
2 image of flag of China China 6 6.74%
3 image of flag of Korea, Republic of Korea, Republic of 3 3.37%
4 image of flag of Brazil Brazil 2 2.25%
5 image of flag of United States United States 1 1.12%
6 image of flag of Taiwan Taiwan 1 1.12%
7 image of flag of Saudi Arabia Saudi Arabia 1 1.12%
8 image of flag of Mexico Mexico 1 1.12%
9 image of flag of Italy Italy 1 1.12%
10 image of flag of Ecuador Ecuador 1 1.12%
    other countries 1 1.12%

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