Multiple Kernel Learning for Remote Sensing Image Classification

作者：Niazmardi Saeid; Demir Begum^*; Bruzzone Lorenzo; Safari Abdolreza; Homayouni Saeid

来源：IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(3): 1425-1443.

DOI：10.1109/TGRS.2017.2762597

• Feature fusion
• kernel-based classification
• multiple kernel learning (MKL)
• remote sensing (RS)

This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category are presented. In particular, MKL algorithms presented only in machine learning are introduced in RS. Then, the investigated MKL algorithms are theoretically compared in terms of their: 1) computational complexities; 2) accuracy with different qualities of kernels; and 3) accuracy with different numbers of kernels. After the theoretical comparison, experimental analyses are carried out to compare different MKL algorithms in terms of: 1) model selection and 2) feature fusion problems. On the basis of the theoretical and experimental analyses of MKL algorithms, some guidelines for a proper selection of the MKL algorithms are derived.

• 出版日期2018-3