EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA
Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-09-01
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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/XLII-4-W4/201/2017/isprs-archives-XLII-4-W4-201-2017.pdf |
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| author | S. Niazmardi A. Safari S. Homayouni |
| author_facet | S. Niazmardi A. Safari S. Homayouni |
| author_sort | S. Niazmardi |
| collection | DOAJ |
| description | Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several
agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is
not straightforward. Because different images of a SITS data have different levels of information regarding the classification
problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification
algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL)
algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS
data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can
be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and
the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop
mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The
experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy
was able to provide better performances when compared to the standard classification algorithm. The results also showed that the
optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy. |
| first_indexed | 2024-12-22T21:23:50Z |
| format | Article |
| id | doaj.art-73e349ba9cf145d5ab6e7a8dc917739e |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-22T21:23:50Z |
| publishDate | 2017-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-73e349ba9cf145d5ab6e7a8dc917739e2022-12-21T18:12:06ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342017-09-01XLII-4-W420120710.5194/isprs-archives-XLII-4-W4-201-2017EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATAS. Niazmardi0A. Safari1S. Homayouni2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, IranDept. of Geography, Environment and Geomatics, U. of Ottawa, CanadaCrop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of information regarding the classification problems. Moreover, the SITS data is a four-dimensional data that cannot be classified using the conventional classification algorithms. To address these issues in this paper, we presented a classification strategy based on Multiple Kernel Learning (MKL) algorithms for SITS data classification. In this strategy, initially different kernels are constructed from different images of the SITS data and then they are combined into a composite kernel using the MKL algorithms. The composite kernel, once constructed, can be used for the classification of the data using the kernel-based classification algorithms. We compared the computational time and the classification performances of the proposed classification strategy using different MKL algorithms for the purpose of crop mapping. The considered MKL algorithms are: MKL-Sum, SimpleMKL, LPMKL and Group-Lasso MKL algorithms. The experimental tests of the proposed strategy on two SITS data sets, acquired by SPOT satellite sensors, showed that this strategy was able to provide better performances when compared to the standard classification algorithm. The results also showed that the optimization method of the used MKL algorithms affects both the computational time and classification accuracy of this strategy.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W4/201/2017/isprs-archives-XLII-4-W4-201-2017.pdf |
| spellingShingle | S. Niazmardi A. Safari S. Homayouni EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA |
| title_full | EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA |
| title_fullStr | EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA |
| title_full_unstemmed | EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA |
| title_short | EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP
MAPPING USING SATELLITE IMAGE TIME-SERIES DATA |
| title_sort | evaluation of multiple kernel learning algorithms for crop mapping using satellite image time series data |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-4-W4/201/2017/isprs-archives-XLII-4-W4-201-2017.pdf |
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