This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with different RS image classification problems are derived.
A comparative study on Multiple Kernel Learning for remote sensing image classification / Niazmardi, Saeid; Demir, Begum; Bruzzone, Lorenzo; Safari, Abdolreza; Homayouni, Saeid. - ELETTRONICO. - (2016), pp. 1512-1515. (Intervento presentato al convegno 36th IEEE International Geoscience and Remote Sensing Symposium: IGARSS 2016 tenutosi a Beijing, China nel 10th-15th July 2016) [10.1109/IGARSS.2016.7729386].
A comparative study on Multiple Kernel Learning for remote sensing image classification
Niazmardi, Saeid;Demir, Begum;Bruzzone, Lorenzo;
2016-01-01
Abstract
This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with different RS image classification problems are derived.| File | Dimensione | Formato | |
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