MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA
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...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2012-09-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-4-W19/115/2011/isprsarchives-XXXVIII-4-W19-115-2011.pdf |
| _version_ | 1819056237327679488 |
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| author | T. Hoberg S. Müller |
| author_facet | T. Hoberg S. Müller |
| author_sort | T. Hoberg |
| collection | DOAJ |
| description | 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 CRF<sub>multi</sub>, 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. |
| first_indexed | 2024-12-21T13:20:13Z |
| format | Article |
| id | doaj.art-59390d878b2d4b80aad672c106dab985 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-21T13:20:13Z |
| publishDate | 2012-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-59390d878b2d4b80aad672c106dab9852022-12-21T19:02:36ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342012-09-01XXXVIII-4-W1911512110.5194/isprsarchives-XXXVIII-4-W19-115-2011MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATAT. Hoberg0S. Müller1IPI – Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, GermanyIPI – Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover, GermanyThe 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 CRF<sub>multi</sub>, 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.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-4-W19/115/2011/isprsarchives-XXXVIII-4-W19-115-2011.pdf |
| spellingShingle | T. Hoberg S. Müller MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA |
| title_full | MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA |
| title_fullStr | MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA |
| title_full_unstemmed | MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA |
| title_short | MULTITEMPORAL CROP TYPE CLASSIFICATION USING CONDITIONAL RANDOM FIELDS AND RAPIDEYE DATA |
| title_sort | multitemporal crop type classification using conditional random fields and rapideye data |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XXXVIII-4-W19/115/2011/isprsarchives-XXXVIII-4-W19-115-2011.pdf |
| work_keys_str_mv | AT thoberg multitemporalcroptypeclassificationusingconditionalrandomfieldsandrapideyedata AT smuller multitemporalcroptypeclassificationusingconditionalrandomfieldsandrapideyedata |