The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 201–207, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-201-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W4, 201–207, 2017
https://doi.org/10.5194/isprs-archives-XLII-4-W4-201-2017

  27 Sep 2017

27 Sep 2017

EVALUATION OF MULTIPLE KERNEL LEARNING ALGORITHMS FOR CROP MAPPING USING SATELLITE IMAGE TIME-SERIES DATA

S. Niazmardi1, A. Safari1, and S. Homayouni2 S. Niazmardi et al.
  • 1School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran
  • 2Dept. of Geography, Environment and Geomatics, U. of Ottawa, Canada

Keywords: Satellite Image Time-series, classification, time-series data, Multiple Kernel Learning, Kernel learning, crop mapping

Abstract. 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.