Multitemporal crop type classification using conditional random fields and rapideye data

Download statistics - Document (COUNTER):

Hoberg, T.; Mueller, S.: Multitemporal crop type classification using conditional random fields and rapideye data. 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. 115-121. DOI:

Repository version

To cite the version in the repository, please use this identifier:

Selected time period:


Sum total of downloads: 280

The task of crop type classification with multitemporal imagery is nowadays often done applying classifiers that are originally developed for single images like support vector machines (SVM). These approaches do not model temporal dependencies in an explicit way. Existing approaches that make use of temporal dependencies are in most cases quite simple and based on rules. Approaches that integrate temporal dependencies to statistical models are very rare and at an early stage of development. Here our approach CRFmulti, based on conditional random fields (CRF), should make a contribution. Conditional random fields consider context knowledge among neighboring primitives in the same way as Markov random fields (MRF) do. Furthermore conditional random fields handle the feature vectors of the neighboring primitives and not only the class labels. Additional to taking spatial context into account, we present an approach for multitemporal data processing where a temporal association potential has been integrated to the common CRF approach to model temporal dependencies. The classification works on pixel-level using spectral image features, whereas all available single images are taken separately. For our experiments a high resolution RapidEye satellite data set of 2010 consisting of 4 images made during the whole vegetation period from April to October is taken. Six crop type categories are distinguished, namely grassland, corn, winter crop, rapeseed, root crops and other crops. To evaluate the potential of the new conditional random field approach the classification result is compared to a manual reference on pixel-and on object-level. Additional a SVM approach is applied under the same conditions and should serve as a benchmark.
License of this version: CC BY 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2011
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 106 37.86%
2 image of flag of United States United States 42 15.00%
3 image of flag of Korea, Republic of Korea, Republic of 18 6.43%
4 image of flag of India India 12 4.29%
5 image of flag of China China 12 4.29%
6 image of flag of Brazil Brazil 10 3.57%
7 image of flag of Australia Australia 8 2.86%
8 image of flag of France France 5 1.79%
9 image of flag of Puerto Rico Puerto Rico 4 1.43%
10 image of flag of Poland Poland 4 1.43%
    other countries 59 21.07%

Further download figures and rankings:


Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository