Abstract
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.
Highlights
Satellite image Time-Series (SITS) data are a collection of satellites images acquired from the same geographical area over a period of time (Jonsson and Eklundh, 2004)
Since most of the Multiple Kernel Learning (MKL) algorithms are proposed based on the Support Vector Machines (SVM), the theory of the MKL algorithms are only proposed for binary classification problems
Obtained results from the classification of both Satellite Image Time-Series (SITS) data sets, using composite kernels obtained from different MKL algorithms are presented in Table 3 and Table 4 for S1 and S2 data sets respectively
Summary
Satellite image Time-Series (SITS) data are a collection of satellites images acquired from the same geographical area over a period of time (Jonsson and Eklundh, 2004). The SITS data, due to their ability to capture the dynamic spectral behaviour of plants and crops during their growing cycles, have been frequently used for different agricultural applications (Jamali et al, 2014) Among these applications, identification of crop types through classification is one of the most important ones (Verhegghen et al, 2014). SITS data that consist of images acquired by multispectral or hyperspectral sensors are categorized as the Multivariate SITS (Adhikari and Agrawal, 2013) This type of SITS, in its original representation, is a four-dimensional data which cannot be classified using the conventional classification algorithms (Baydogan and Runger, 2015). The stacked image can be very high dimensional
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